diff --git a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/fcnet_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/fcnet_cifar10_image_classifier.yaml
index 8e762f3f..2e8213d2 100644
--- a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/fcnet_cifar10_image_classifier.yaml
+++ b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/fcnet_cifar10_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.126e+00'
-  mean: '-6.179e-03'
+  mean: '6.869e-03'
   min: '-1.989e+00'
   shape:
   - 128
   - 3
   - 32
   - 32
-  sum: '-2.43e+03'
+  sum: '2.701e+03'
 batch.1:
   device: cuda:0
   max: 9
@@ -19,71 +19,71 @@ batch.1:
   sum: 583
 grads.network.0.1.bias:
   device: cuda:0
-  max: '6.107e-03'
-  mean: '1.775e-04'
-  min: '-5.292e-03'
+  max: '5.928e-03'
+  mean: '3.020e-04'
+  min: '-3.916e-03'
   shape:
   - 128
-  sum: '2.272e-02'
+  sum: '3.866e-02'
 grads.network.0.1.weight:
   device: cuda:0
-  max: '1.307e-02'
-  mean: '4.693e-05'
-  min: '-1.141e-02'
+  max: '1.229e-02'
+  mean: '1.095e-04'
+  min: '-1.115e-02'
   shape:
   - 128
   - 3072
-  sum: '1.845e+01'
+  sum: '4.306e+01'
 grads.network.1.0.bias:
   device: cuda:0
-  max: '1.041e-02'
-  mean: '6.975e-04'
-  min: '-8.782e-03'
+  max: '1.187e-02'
+  mean: '6.403e-04'
+  min: '-9.623e-03'
   shape:
   - 128
-  sum: '8.928e-02'
+  sum: '8.196e-02'
 grads.network.1.0.weight:
   device: cuda:0
-  max: '1.584e-02'
-  mean: '1.481e-04'
-  min: '-1.507e-02'
+  max: '1.566e-02'
+  mean: '1.344e-04'
+  min: '-1.467e-02'
   shape:
   - 128
   - 128
-  sum: '2.426e+00'
+  sum: '2.202e+00'
 grads.network.2.0.bias:
   device: cuda:0
-  max: '3.282e-02'
-  mean: '-1.956e-09'
-  min: '-2.134e-02'
+  max: '3.269e-02'
+  mean: '-2.887e-09'
+  min: '-2.157e-02'
   shape:
   - 10
-  sum: '-1.956e-08'
+  sum: '-2.887e-08'
 grads.network.2.0.weight:
   device: cuda:0
-  max: '2.200e-02'
-  mean: '-2.561e-10'
-  min: '-5.831e-02'
+  max: '2.914e-02'
+  mean: '-2.98e-10'
+  min: '-3.501e-02'
   shape:
   - 10
   - 128
-  sum: '-3.278e-07'
+  sum: '-3.814e-07'
 outputs.logits:
   device: cuda:0
-  max: '7.036e-01'
-  mean: '-8.651e-03'
-  min: '-8.180e-01'
+  max: '8.135e-01'
+  mean: '-8.627e-03'
+  min: '-7.944e-01'
   shape:
   - 128
   - 10
-  sum: '-1.107e+01'
+  sum: '-1.104e+01'
 outputs.loss:
   device: cuda:0
-  max: '2.316e+00'
-  mean: '2.316e+00'
-  min: '2.316e+00'
+  max: '2.319e+00'
+  mean: '2.319e+00'
+  min: '2.319e+00'
   shape: []
-  sum: '2.316e+00'
+  sum: '2.319e+00'
 outputs.y:
   device: cuda:0
   max: 9
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/fcnet_fashion_mnist_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/fcnet_fashion_mnist_image_classifier.yaml
index 8be326eb..7c7195be 100644
--- a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/fcnet_fashion_mnist_image_classifier.yaml
+++ b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/fcnet_fashion_mnist_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.821e+00'
-  mean: '4.822e-01'
+  mean: '4.772e-01'
   min: '-4.242e-01'
   shape:
   - 128
   - 1
   - 28
   - 28
-  sum: '4.839e+04'
+  sum: '4.789e+04'
 batch.1:
   device: cuda:0
   max: 9
@@ -19,71 +19,71 @@ batch.1:
   sum: 583
 grads.network.0.1.bias:
   device: cuda:0
-  max: '6.875e-03'
-  mean: '2.096e-04'
-  min: '-8.370e-03'
+  max: '7.419e-03'
+  mean: '4.543e-04'
+  min: '-4.832e-03'
   shape:
   - 128
-  sum: '2.683e-02'
+  sum: '5.816e-02'
 grads.network.0.1.weight:
   device: cuda:0
-  max: '1.948e-02'
-  mean: '2.916e-04'
-  min: '-2.213e-02'
+  max: '1.735e-02'
+  mean: '2.23e-04'
+  min: '-1.552e-02'
   shape:
   - 128
   - 784
-  sum: '2.926e+01'
+  sum: '2.238e+01'
 grads.network.1.0.bias:
   device: cuda:0
-  max: '1.109e-02'
-  mean: '2.213e-04'
-  min: '-1.267e-02'
+  max: '1.157e-02'
+  mean: '2.873e-04'
+  min: '-1.017e-02'
   shape:
   - 128
-  sum: '2.832e-02'
+  sum: '3.678e-02'
 grads.network.1.0.weight:
   device: cuda:0
-  max: '2.374e-02'
-  mean: '9.326e-05'
-  min: '-2.32e-02'
+  max: '2.752e-02'
+  mean: '1.217e-04'
+  min: '-3.079e-02'
   shape:
   - 128
   - 128
-  sum: '1.528e+00'
+  sum: '1.994e+00'
 grads.network.2.0.bias:
   device: cuda:0
-  max: '3.847e-02'
-  mean: '-3.353e-09'
-  min: '-4.706e-02'
+  max: '3.865e-02'
+  mean: '-9.313e-10'
+  min: '-4.547e-02'
   shape:
   - 10
-  sum: '-3.353e-08'
+  sum: '-9.313e-09'
 grads.network.2.0.weight:
   device: cuda:0
-  max: '5.741e-02'
-  mean: '-3.929e-10'
-  min: '-6.431e-02'
+  max: '4.74e-02'
+  mean: '-2.085e-10'
+  min: '-6.661e-02'
   shape:
   - 10
   - 128
-  sum: '-5.029e-07'
+  sum: '-2.668e-07'
 outputs.logits:
   device: cuda:0
-  max: '9.872e-01'
-  mean: '-1.288e-02'
-  min: '-7.225e-01'
+  max: '8.907e-01'
+  mean: '-1.669e-02'
+  min: '-6.486e-01'
   shape:
   - 128
   - 10
-  sum: '-1.648e+01'
+  sum: '-2.136e+01'
 outputs.loss:
   device: cuda:0
-  max: '2.311e+00'
-  mean: '2.311e+00'
-  min: '2.311e+00'
+  max: '2.309e+00'
+  mean: '2.309e+00'
+  min: '2.309e+00'
   shape: []
-  sum: '2.311e+00'
+  sum: '2.309e+00'
 outputs.y:
   device: cuda:0
   max: 9
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/fcnet_mnist_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/fcnet_mnist_image_classifier.yaml
index 232a8e50..17e7c8bb 100644
--- a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/fcnet_mnist_image_classifier.yaml
+++ b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/fcnet_mnist_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.821e+00'
-  mean: '1.432e-02'
+  mean: '1.477e-02'
   min: '-4.242e-01'
   shape:
   - 128
   - 1
   - 28
   - 28
-  sum: '1.437e+03'
+  sum: '1.482e+03'
 batch.1:
   device: cuda:0
   max: 9
@@ -19,71 +19,71 @@ batch.1:
   sum: 543
 grads.network.0.1.bias:
   device: cuda:0
-  max: '1.075e-02'
-  mean: '2.421e-04'
-  min: '-7.844e-03'
+  max: '8.396e-03'
+  mean: '1.867e-04'
+  min: '-6.027e-03'
   shape:
   - 128
-  sum: '3.099e-02'
+  sum: '2.389e-02'
 grads.network.0.1.weight:
   device: cuda:0
-  max: '2.006e-02'
-  mean: '5.258e-05'
-  min: '-1.844e-02'
+  max: '1.893e-02'
+  mean: '4.891e-05'
+  min: '-1.587e-02'
   shape:
   - 128
   - 784
-  sum: '5.277e+00'
+  sum: '4.909e+00'
 grads.network.1.0.bias:
   device: cuda:0
-  max: '1.169e-02'
-  mean: '4.285e-04'
+  max: '1.069e-02'
+  mean: '7.139e-05'
   min: '-1.152e-02'
   shape:
   - 128
-  sum: '5.485e-02'
+  sum: '9.138e-03'
 grads.network.1.0.weight:
   device: cuda:0
-  max: '1.753e-02'
-  mean: '1.016e-04'
-  min: '-2.219e-02'
+  max: '1.619e-02'
+  mean: '3.114e-05'
+  min: '-1.955e-02'
   shape:
   - 128
   - 128
-  sum: '1.665e+00'
+  sum: '5.102e-01'
 grads.network.2.0.bias:
   device: cuda:0
-  max: '3.969e-02'
-  mean: '-1.490e-09'
-  min: '-7.979e-02'
+  max: '3.893e-02'
+  mean: '-7.451e-10'
+  min: '-7.559e-02'
   shape:
   - 10
-  sum: '-1.490e-08'
+  sum: '-7.451e-09'
 grads.network.2.0.weight:
   device: cuda:0
-  max: '3.221e-02'
-  mean: '-1.928e-10'
-  min: '-6.755e-02'
+  max: '3.259e-02'
+  mean: '-9.604e-11'
+  min: '-4.695e-02'
   shape:
   - 10
   - 128
-  sum: '-2.468e-07'
+  sum: '-1.229e-07'
 outputs.logits:
   device: cuda:0
-  max: '7.029e-01'
-  mean: '-3.564e-02'
-  min: '-7.781e-01'
+  max: '6.222e-01'
+  mean: '-3.729e-02'
+  min: '-6.079e-01'
   shape:
   - 128
   - 10
-  sum: '-4.562e+01'
+  sum: '-4.773e+01'
 outputs.loss:
   device: cuda:0
-  max: '2.304e+00'
-  mean: '2.304e+00'
-  min: '2.304e+00'
+  max: '2.308e+00'
+  mean: '2.308e+00'
+  min: '2.308e+00'
   shape: []
-  sum: '2.304e+00'
+  sum: '2.308e+00'
 outputs.y:
   device: cuda:0
   max: 9
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet18_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet18_cifar10_image_classifier.yaml
index 1ada67d1..4a60edb5 100644
--- a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet18_cifar10_image_classifier.yaml
+++ b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet18_cifar10_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.126e+00'
-  mean: '-6.179e-03'
+  mean: '6.869e-03'
   min: '-1.989e+00'
   shape:
   - 128
   - 3
   - 32
   - 32
-  sum: '-2.43e+03'
+  sum: '2.701e+03'
 batch.1:
   device: cuda:0
   max: 9
@@ -19,577 +19,577 @@ batch.1:
   sum: 583
 grads.network.bn1.bias:
   device: cuda:0
-  max: '4.94e-02'
-  mean: '3.131e-04'
-  min: '-4.549e-02'
+  max: '4.78e-02'
+  mean: '1.011e-03'
+  min: '-5.382e-02'
   shape:
   - 64
-  sum: '2.004e-02'
+  sum: '6.471e-02'
 grads.network.bn1.weight:
   device: cuda:0
-  max: '7.001e-02'
-  mean: '1.024e-03'
-  min: '-7.857e-02'
+  max: '1.036e-01'
+  mean: '3.811e-03'
+  min: '-1.129e-01'
   shape:
   - 64
-  sum: '6.554e-02'
+  sum: '2.439e-01'
 grads.network.conv1.weight:
   device: cuda:0
-  max: '6.192e-01'
-  mean: '1.341e-03'
-  min: '-7.564e-01'
+  max: '6.393e-01'
+  mean: '4.047e-03'
+  min: '-7.638e-01'
   shape:
   - 64
   - 3
   - 7
   - 7
-  sum: '1.261e+01'
+  sum: '3.808e+01'
 grads.network.fc.bias:
   device: cuda:0
-  max: '8.718e-02'
-  mean: '-2.235e-09'
-  min: '-7.594e-02'
+  max: '9.090e-02'
+  mean: '-7.451e-10'
+  min: '-7.546e-02'
   shape:
   - 10
-  sum: '-2.235e-08'
+  sum: '-7.451e-09'
 grads.network.fc.weight:
   device: cuda:0
-  max: '1.526e-01'
-  mean: '-7.902e-10'
-  min: '-1.636e-01'
+  max: '1.961e-01'
+  mean: '-6.585e-11'
+  min: '-1.625e-01'
   shape:
   - 10
   - 512
-  sum: '-4.046e-06'
+  sum: '-3.371e-07'
 grads.network.layer1.0.bn1.bias:
   device: cuda:0
-  max: '4.809e-02'
-  mean: '-6.887e-05'
-  min: '-4.261e-02'
+  max: '4.185e-02'
+  mean: '1.05e-03'
+  min: '-3.98e-02'
   shape:
   - 64
-  sum: '-4.407e-03'
+  sum: '6.719e-02'
 grads.network.layer1.0.bn1.weight:
   device: cuda:0
-  max: '5.681e-02'
-  mean: '-2.87e-08'
-  min: '-6.472e-02'
+  max: '5.675e-02'
+  mean: '-1.997e-08'
+  min: '-3.615e-02'
   shape:
   - 64
-  sum: '-1.837e-06'
+  sum: '-1.278e-06'
 grads.network.layer1.0.bn2.bias:
   device: cuda:0
-  max: '2.823e-02'
-  mean: '6.060e-04'
-  min: '-3.829e-02'
+  max: '3.156e-02'
+  mean: '9.212e-04'
+  min: '-2.666e-02'
   shape:
   - 64
-  sum: '3.878e-02'
+  sum: '5.896e-02'
 grads.network.layer1.0.bn2.weight:
   device: cuda:0
-  max: '4.298e-02'
-  mean: '-1.402e-03'
-  min: '-5.307e-02'
+  max: '3.506e-02'
+  mean: '-1.287e-03'
+  min: '-4.588e-02'
   shape:
   - 64
-  sum: '-8.975e-02'
+  sum: '-8.239e-02'
 grads.network.layer1.0.conv1.weight:
   device: cuda:0
-  max: '1.152e-01'
-  mean: '2.658e-05'
-  min: '-1.006e-01'
+  max: '1.082e-01'
+  mean: '9.125e-04'
+  min: '-9.543e-02'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '9.8e-01'
+  sum: '3.364e+01'
 grads.network.layer1.0.conv2.weight:
   device: cuda:0
-  max: '7.023e-02'
-  mean: '2.208e-04'
-  min: '-8.426e-02'
+  max: '7.375e-02'
+  mean: '1.914e-04'
+  min: '-8.228e-02'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '8.138e+00'
+  sum: '7.057e+00'
 grads.network.layer1.1.bn1.bias:
   device: cuda:0
-  max: '5.121e-02'
-  mean: '1.57e-05'
-  min: '-3.888e-02'
+  max: '4.352e-02'
+  mean: '1.476e-03'
+  min: '-3.282e-02'
   shape:
   - 64
-  sum: '1.005e-03'
+  sum: '9.445e-02'
 grads.network.layer1.1.bn1.weight:
   device: cuda:0
-  max: '3.775e-02'
-  mean: '4.075e-09'
-  min: '-3.404e-02'
+  max: '4.861e-02'
+  mean: '-1.851e-08'
+  min: '-3.913e-02'
   shape:
   - 64
-  sum: '2.608e-07'
+  sum: '-1.185e-06'
 grads.network.layer1.1.bn2.bias:
   device: cuda:0
-  max: '2.051e-02'
-  mean: '1.167e-03'
-  min: '-2.095e-02'
+  max: '1.762e-02'
+  mean: '1.206e-03'
+  min: '-1.477e-02'
   shape:
   - 64
-  sum: '7.466e-02'
+  sum: '7.718e-02'
 grads.network.layer1.1.bn2.weight:
   device: cuda:0
-  max: '3.145e-02'
-  mean: '3.783e-04'
-  min: '-3.695e-02'
+  max: '3.082e-02'
+  mean: '-2.523e-03'
+  min: '-3.858e-02'
   shape:
   - 64
-  sum: '2.421e-02'
+  sum: '-1.615e-01'
 grads.network.layer1.1.conv1.weight:
   device: cuda:0
-  max: '7.035e-02'
-  mean: '-9.996e-04'
-  min: '-7.167e-02'
+  max: '8.595e-02'
+  mean: '-3.158e-04'
+  min: '-7.017e-02'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '-3.685e+01'
+  sum: '-1.164e+01'
 grads.network.layer1.1.conv2.weight:
   device: cuda:0
-  max: '7.708e-02'
-  mean: '3.07e-04'
-  min: '-5.375e-02'
+  max: '5.951e-02'
+  mean: '4.442e-04'
+  min: '-5.832e-02'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '1.132e+01'
+  sum: '1.638e+01'
 grads.network.layer2.0.bn1.bias:
   device: cuda:0
-  max: '2.687e-02'
-  mean: '5.859e-04'
-  min: '-2.458e-02'
+  max: '2.166e-02'
+  mean: '-7.185e-04'
+  min: '-3.071e-02'
   shape:
   - 128
-  sum: '7.500e-02'
+  sum: '-9.196e-02'
 grads.network.layer2.0.bn1.weight:
   device: cuda:0
-  max: '2.383e-02'
-  mean: '-1.983e-08'
-  min: '-3.218e-02'
+  max: '3.093e-02'
+  mean: '-1.845e-08'
+  min: '-2.897e-02'
   shape:
   - 128
-  sum: '-2.539e-06'
+  sum: '-2.362e-06'
 grads.network.layer2.0.bn2.bias:
   device: cuda:0
-  max: '1.778e-02'
-  mean: '-7.097e-04'
-  min: '-2.318e-02'
+  max: '2.307e-02'
+  mean: '-4.022e-04'
+  min: '-2.904e-02'
   shape:
   - 128
-  sum: '-9.084e-02'
+  sum: '-5.148e-02'
 grads.network.layer2.0.bn2.weight:
   device: cuda:0
-  max: '2.506e-02'
-  mean: '-1.001e-03'
-  min: '-2.575e-02'
+  max: '2.944e-02'
+  mean: '-7.596e-04'
+  min: '-3.252e-02'
   shape:
   - 128
-  sum: '-1.281e-01'
+  sum: '-9.723e-02'
 grads.network.layer2.0.conv1.weight:
   device: cuda:0
-  max: '7.148e-02'
-  mean: '8.56e-04'
-  min: '-6.533e-02'
+  max: '6.9e-02'
+  mean: '-5.9e-04'
+  min: '-7.574e-02'
   shape:
   - 128
   - 64
   - 3
   - 3
-  sum: '6.311e+01'
+  sum: '-4.35e+01'
 grads.network.layer2.0.conv2.weight:
   device: cuda:0
-  max: '4.581e-02'
-  mean: '5.887e-06'
-  min: '-4.373e-02'
+  max: '4.737e-02'
+  mean: '3.349e-04'
+  min: '-4.567e-02'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '8.681e-01'
+  sum: '4.939e+01'
 grads.network.layer2.0.downsample.0.weight:
   device: cuda:0
-  max: '5.408e-02'
-  mean: '6.587e-05'
-  min: '-6.218e-02'
+  max: '4.541e-02'
+  mean: '4.904e-04'
+  min: '-5.362e-02'
   shape:
   - 128
   - 64
   - 1
   - 1
-  sum: '5.396e-01'
+  sum: '4.017e+00'
 grads.network.layer2.0.downsample.1.bias:
   device: cuda:0
-  max: '1.778e-02'
-  mean: '-7.097e-04'
-  min: '-2.318e-02'
+  max: '2.307e-02'
+  mean: '-4.022e-04'
+  min: '-2.904e-02'
   shape:
   - 128
-  sum: '-9.084e-02'
+  sum: '-5.148e-02'
 grads.network.layer2.0.downsample.1.weight:
   device: cuda:0
-  max: '2.67e-02'
-  mean: '7.026e-04'
-  min: '-2.834e-02'
+  max: '3.453e-02'
+  mean: '6.507e-04'
+  min: '-2.165e-02'
   shape:
   - 128
-  sum: '8.994e-02'
+  sum: '8.329e-02'
 grads.network.layer2.1.bn1.bias:
   device: cuda:0
-  max: '2.282e-02'
-  mean: '4.179e-04'
-  min: '-1.989e-02'
+  max: '1.999e-02'
+  mean: '5.68e-04'
+  min: '-2.425e-02'
   shape:
   - 128
-  sum: '5.349e-02'
+  sum: '7.270e-02'
 grads.network.layer2.1.bn1.weight:
   device: cuda:0
-  max: '2.738e-02'
-  mean: '3.492e-09'
-  min: '-2.028e-02'
+  max: '2.542e-02'
+  mean: '1.572e-09'
+  min: '-2.060e-02'
   shape:
   - 128
-  sum: '4.470e-07'
+  sum: '2.012e-07'
 grads.network.layer2.1.bn2.bias:
   device: cuda:0
-  max: '1.634e-02'
-  mean: '4.516e-04'
-  min: '-1.524e-02'
+  max: '2.059e-02'
+  mean: '4.267e-04'
+  min: '-1.558e-02'
   shape:
   - 128
-  sum: '5.78e-02'
+  sum: '5.461e-02'
 grads.network.layer2.1.bn2.weight:
   device: cuda:0
-  max: '2.251e-02'
-  mean: '2.985e-04'
-  min: '-2.765e-02'
+  max: '1.791e-02'
+  mean: '1.089e-04'
+  min: '-1.751e-02'
   shape:
   - 128
-  sum: '3.821e-02'
+  sum: '1.394e-02'
 grads.network.layer2.1.conv1.weight:
   device: cuda:0
-  max: '4.786e-02'
-  mean: '-1.842e-04'
-  min: '-4.788e-02'
+  max: '3.998e-02'
+  mean: '4.761e-05'
+  min: '-4.121e-02'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '-2.716e+01'
+  sum: '7.021e+00'
 grads.network.layer2.1.conv2.weight:
   device: cuda:0
-  max: '3.281e-02'
-  mean: '-1.638e-05'
-  min: '-3.597e-02'
+  max: '3.434e-02'
+  mean: '1.126e-04'
+  min: '-4.169e-02'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '-2.415e+00'
+  sum: '1.661e+01'
 grads.network.layer3.0.bn1.bias:
   device: cuda:0
-  max: '1.373e-02'
-  mean: '-1.949e-05'
-  min: '-1.339e-02'
+  max: '1.454e-02'
+  mean: '-2.541e-04'
+  min: '-1.473e-02'
   shape:
   - 256
-  sum: '-4.989e-03'
+  sum: '-6.504e-02'
 grads.network.layer3.0.bn1.weight:
   device: cuda:0
-  max: '1.651e-02'
-  mean: '-1.778e-08'
-  min: '-1.433e-02'
+  max: '1.757e-02'
+  mean: '-6.898e-09'
+  min: '-1.498e-02'
   shape:
   - 256
-  sum: '-4.552e-06'
+  sum: '-1.766e-06'
 grads.network.layer3.0.bn2.bias:
   device: cuda:0
-  max: '1.342e-02'
-  mean: '-1.425e-04'
-  min: '-1.272e-02'
+  max: '1.005e-02'
+  mean: '-2.549e-04'
+  min: '-1.117e-02'
   shape:
   - 256
-  sum: '-3.647e-02'
+  sum: '-6.524e-02'
 grads.network.layer3.0.bn2.weight:
   device: cuda:0
-  max: '1.591e-02'
-  mean: '-4.350e-04'
-  min: '-1.678e-02'
+  max: '1.203e-02'
+  mean: '-1.802e-04'
+  min: '-1.230e-02'
   shape:
   - 256
-  sum: '-1.114e-01'
+  sum: '-4.614e-02'
 grads.network.layer3.0.conv1.weight:
   device: cuda:0
-  max: '3.91e-02'
-  mean: '1.103e-04'
-  min: '-3.65e-02'
+  max: '4.111e-02'
+  mean: '-2.892e-05'
+  min: '-4.500e-02'
   shape:
   - 256
   - 128
   - 3
   - 3
-  sum: '3.254e+01'
+  sum: '-8.528e+00'
 grads.network.layer3.0.conv2.weight:
   device: cuda:0
-  max: '2.947e-02'
-  mean: '-2.338e-05'
-  min: '-3.166e-02'
+  max: '3.413e-02'
+  mean: '-4.338e-05'
+  min: '-2.915e-02'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-1.379e+01'
+  sum: '-2.558e+01'
 grads.network.layer3.0.downsample.0.weight:
   device: cuda:0
-  max: '3.125e-02'
-  mean: '-1.221e-06'
-  min: '-2.705e-02'
+  max: '2.549e-02'
+  mean: '-1.998e-05'
+  min: '-3.279e-02'
   shape:
   - 256
   - 128
   - 1
   - 1
-  sum: '-4.002e-02'
+  sum: '-6.548e-01'
 grads.network.layer3.0.downsample.1.bias:
   device: cuda:0
-  max: '1.342e-02'
-  mean: '-1.425e-04'
-  min: '-1.272e-02'
+  max: '1.005e-02'
+  mean: '-2.549e-04'
+  min: '-1.117e-02'
   shape:
   - 256
-  sum: '-3.647e-02'
+  sum: '-6.524e-02'
 grads.network.layer3.0.downsample.1.weight:
   device: cuda:0
-  max: '1.214e-02'
-  mean: '5.825e-05'
-  min: '-1.422e-02'
+  max: '1.516e-02'
+  mean: '2.290e-04'
+  min: '-1.29e-02'
   shape:
   - 256
-  sum: '1.491e-02'
+  sum: '5.863e-02'
 grads.network.layer3.1.bn1.bias:
   device: cuda:0
-  max: '1.198e-02'
-  mean: '1.985e-04'
-  min: '-9.063e-03'
+  max: '1.016e-02'
+  mean: '-1.763e-04'
+  min: '-1.080e-02'
   shape:
   - 256
-  sum: '5.082e-02'
+  sum: '-4.513e-02'
 grads.network.layer3.1.bn1.weight:
   device: cuda:0
-  max: '1.364e-02'
-  mean: '1.119e-08'
-  min: '-1.406e-02'
+  max: '1.155e-02'
+  mean: '-1.834e-09'
+  min: '-1.763e-02'
   shape:
   - 256
-  sum: '2.865e-06'
+  sum: '-4.694e-07'
 grads.network.layer3.1.bn2.bias:
   device: cuda:0
-  max: '6.948e-03'
-  mean: '1.387e-04'
-  min: '-6.29e-03'
+  max: '7.769e-03'
+  mean: '1.617e-05'
+  min: '-9.776e-03'
   shape:
   - 256
-  sum: '3.551e-02'
+  sum: '4.140e-03'
 grads.network.layer3.1.bn2.weight:
   device: cuda:0
-  max: '1.099e-02'
-  mean: '3.768e-04'
-  min: '-1.145e-02'
+  max: '8.94e-03'
+  mean: '-4.878e-05'
+  min: '-1.173e-02'
   shape:
   - 256
-  sum: '9.646e-02'
+  sum: '-1.249e-02'
 grads.network.layer3.1.conv1.weight:
   device: cuda:0
-  max: '2.413e-02'
-  mean: '-6.619e-06'
-  min: '-2.651e-02'
+  max: '3.196e-02'
+  mean: '-4.379e-05'
+  min: '-2.562e-02'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-3.904e+00'
+  sum: '-2.583e+01'
 grads.network.layer3.1.conv2.weight:
   device: cuda:0
-  max: '2.347e-02'
-  mean: '-3.211e-05'
-  min: '-2.596e-02'
+  max: '2.427e-02'
+  mean: '-3.177e-05'
+  min: '-2.463e-02'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-1.894e+01'
+  sum: '-1.874e+01'
 grads.network.layer4.0.bn1.bias:
   device: cuda:0
-  max: '6.987e-03'
-  mean: '-5.95e-06'
-  min: '-6.451e-03'
+  max: '5.839e-03'
+  mean: '-2.881e-05'
+  min: '-5.929e-03'
   shape:
   - 512
-  sum: '-3.046e-03'
+  sum: '-1.475e-02'
 grads.network.layer4.0.bn1.weight:
   device: cuda:0
-  max: '8.782e-03'
-  mean: '5.227e-08'
-  min: '-8.326e-03'
+  max: '6.665e-03'
+  mean: '5.733e-08'
+  min: '-7.679e-03'
   shape:
   - 512
-  sum: '2.676e-05'
+  sum: '2.935e-05'
 grads.network.layer4.0.bn2.bias:
   device: cuda:0
-  max: '7.944e-03'
-  mean: '4.654e-04'
-  min: '-5.159e-03'
+  max: '7.407e-03'
+  mean: '4.676e-04'
+  min: '-6.303e-03'
   shape:
   - 512
-  sum: '2.383e-01'
+  sum: '2.394e-01'
 grads.network.layer4.0.bn2.weight:
   device: cuda:0
-  max: '7.365e-03'
-  mean: '3.815e-04'
-  min: '-7.759e-03'
+  max: '1.043e-02'
+  mean: '4.088e-04'
+  min: '-7.583e-03'
   shape:
   - 512
-  sum: '1.953e-01'
+  sum: '2.093e-01'
 grads.network.layer4.0.conv1.weight:
   device: cuda:0
-  max: '3.395e-02'
-  mean: '1.298e-05'
-  min: '-3.451e-02'
+  max: '3.876e-02'
+  mean: '-3.794e-05'
+  min: '-3.168e-02'
   shape:
   - 512
   - 256
   - 3
   - 3
-  sum: '1.531e+01'
+  sum: '-4.475e+01'
 grads.network.layer4.0.conv2.weight:
   device: cuda:0
-  max: '2.825e-02'
-  mean: '-1.254e-06'
-  min: '-2.923e-02'
+  max: '3.124e-02'
+  mean: '1.423e-05'
+  min: '-3.141e-02'
   shape:
   - 512
   - 512
   - 3
   - 3
-  sum: '-2.96e+00'
+  sum: '3.357e+01'
 grads.network.layer4.0.downsample.0.weight:
   device: cuda:0
-  max: '1.519e-02'
-  mean: '2.644e-06'
-  min: '-1.993e-02'
+  max: '1.491e-02'
+  mean: '4.278e-05'
+  min: '-2.249e-02'
   shape:
   - 512
   - 256
   - 1
   - 1
-  sum: '3.466e-01'
+  sum: '5.607e+00'
 grads.network.layer4.0.downsample.1.bias:
   device: cuda:0
-  max: '7.944e-03'
-  mean: '4.654e-04'
-  min: '-5.159e-03'
+  max: '7.407e-03'
+  mean: '4.676e-04'
+  min: '-6.303e-03'
   shape:
   - 512
-  sum: '2.383e-01'
+  sum: '2.394e-01'
 grads.network.layer4.0.downsample.1.weight:
   device: cuda:0
-  max: '6.664e-03'
-  mean: '3.273e-04'
-  min: '-6.98e-03'
+  max: '8.099e-03'
+  mean: '3.919e-04'
+  min: '-8.998e-03'
   shape:
   - 512
-  sum: '1.676e-01'
+  sum: '2.006e-01'
 grads.network.layer4.1.bn1.bias:
   device: cuda:0
-  max: '5.407e-03'
-  mean: '9.024e-05'
-  min: '-4.404e-03'
+  max: '4.556e-03'
+  mean: '9.602e-06'
+  min: '-5.234e-03'
   shape:
   - 512
-  sum: '4.620e-02'
+  sum: '4.916e-03'
 grads.network.layer4.1.bn1.weight:
   device: cuda:0
-  max: '5.791e-03'
-  mean: '4.913e-08'
-  min: '-5.188e-03'
+  max: '5.446e-03'
+  mean: '4.256e-08'
+  min: '-9.259e-03'
   shape:
   - 512
-  sum: '2.515e-05'
+  sum: '2.179e-05'
 grads.network.layer4.1.bn2.bias:
   device: cuda:0
-  max: '8.746e-03'
-  mean: '4.971e-04'
-  min: '-9.116e-03'
+  max: '6.931e-03'
+  mean: '5.733e-04'
+  min: '-9.201e-03'
   shape:
   - 512
-  sum: '2.545e-01'
+  sum: '2.935e-01'
 grads.network.layer4.1.bn2.weight:
   device: cuda:0
-  max: '6.717e-03'
-  mean: '3.269e-04'
-  min: '-5.782e-03'
+  max: '6.534e-03'
+  mean: '3.358e-04'
+  min: '-5.669e-03'
   shape:
   - 512
-  sum: '1.674e-01'
+  sum: '1.719e-01'
 grads.network.layer4.1.conv1.weight:
   device: cuda:0
-  max: '2.951e-02'
-  mean: '-5.57e-06'
-  min: '-3.434e-02'
+  max: '3.491e-02'
+  mean: '1.222e-06'
+  min: '-3.205e-02'
   shape:
   - 512
   - 512
   - 3
   - 3
-  sum: '-1.314e+01'
+  sum: '2.883e+00'
 grads.network.layer4.1.conv2.weight:
   device: cuda:0
-  max: '2.492e-02'
-  mean: '-1.259e-06'
-  min: '-2.262e-02'
+  max: '2.070e-02'
+  mean: '3.459e-06'
+  min: '-2.459e-02'
   shape:
   - 512
   - 512
   - 3
   - 3
-  sum: '-2.971e+00'
+  sum: '8.16e+00'
 outputs.logits:
   device: cuda:0
-  max: '2.728e+00'
-  mean: '8.106e-02'
-  min: '-2.536e+00'
+  max: '3.632e+00'
+  mean: '7.657e-02'
+  min: '-2.666e+00'
   shape:
   - 128
   - 10
-  sum: '1.038e+02'
+  sum: '9.801e+01'
 outputs.loss:
   device: cuda:0
-  max: '2.593e+00'
-  mean: '2.593e+00'
-  min: '2.593e+00'
+  max: '2.657e+00'
+  mean: '2.657e+00'
+  min: '2.657e+00'
   shape: []
-  sum: '2.593e+00'
+  sum: '2.657e+00'
 outputs.y:
   device: cuda:0
   max: 9
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet18_imagenet_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet18_imagenet_image_classifier.yaml
index 938d81f2..11bdf31c 100644
--- a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet18_imagenet_image_classifier.yaml
+++ b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet18_imagenet_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.640e+00'
-  mean: '-6.663e-02'
+  mean: '-6.142e-02'
   min: '-2.118e+00'
   shape:
   - 64
   - 3
   - 224
   - 224
-  sum: '-6.419e+05'
+  sum: '-5.917e+05'
 batch.1:
   device: cuda:0
   max: 988
@@ -19,577 +19,577 @@ batch.1:
   sum: 33166
 grads.network.bn1.bias:
   device: cuda:0
-  max: '1.433e-02'
-  mean: '1.035e-03'
-  min: '-1.257e-02'
+  max: '1.271e-02'
+  mean: '-1.027e-04'
+  min: '-1.268e-02'
   shape:
   - 64
-  sum: '6.621e-02'
+  sum: '-6.573e-03'
 grads.network.bn1.weight:
   device: cuda:0
-  max: '1.866e-02'
-  mean: '9.764e-05'
-  min: '-2.028e-02'
+  max: '1.774e-02'
+  mean: '-8.635e-05'
+  min: '-1.674e-02'
   shape:
   - 64
-  sum: '6.249e-03'
+  sum: '-5.527e-03'
 grads.network.conv1.weight:
   device: cuda:0
-  max: '1.798e-01'
-  mean: '6.264e-03'
-  min: '-1.354e-01'
+  max: '2.109e-01'
+  mean: '3.684e-03'
+  min: '-1.847e-01'
   shape:
   - 64
   - 3
   - 7
   - 7
-  sum: '5.893e+01'
+  sum: '3.466e+01'
 grads.network.fc.bias:
   device: cuda:0
-  max: '3.523e-03'
-  mean: '2.235e-11'
+  max: '3.518e-03'
+  mean: '2.980e-11'
   min: '-3.062e-02'
   shape:
   - 1000
-  sum: '2.235e-08'
+  sum: '2.980e-08'
 grads.network.fc.weight:
   device: cuda:0
-  max: '4.594e-03'
-  mean: '1.490e-11'
-  min: '-8.777e-02'
+  max: '4.303e-03'
+  mean: '2.980e-11'
+  min: '-7.610e-02'
   shape:
   - 1000
   - 512
-  sum: '7.629e-06'
+  sum: '1.526e-05'
 grads.network.layer1.0.bn1.bias:
   device: cuda:0
-  max: '1.035e-02'
-  mean: '-8.887e-05'
-  min: '-1.081e-02'
+  max: '8.641e-03'
+  mean: '-7.812e-04'
+  min: '-8.647e-03'
   shape:
   - 64
-  sum: '-5.688e-03'
+  sum: '-5.e-02'
 grads.network.layer1.0.bn1.weight:
   device: cuda:0
-  max: '1.322e-02'
-  mean: '3.085e-09'
-  min: '-1.446e-02'
+  max: '9.976e-03'
+  mean: '4.162e-09'
+  min: '-9.564e-03'
   shape:
   - 64
-  sum: '1.974e-07'
+  sum: '2.664e-07'
 grads.network.layer1.0.bn2.bias:
   device: cuda:0
-  max: '5.771e-03'
-  mean: '2.727e-04'
-  min: '-8.209e-03'
+  max: '4.759e-03'
+  mean: '-4.058e-05'
+  min: '-6.041e-03'
   shape:
   - 64
-  sum: '1.745e-02'
+  sum: '-2.597e-03'
 grads.network.layer1.0.bn2.weight:
   device: cuda:0
-  max: '9.735e-03'
-  mean: '3.428e-05'
-  min: '-7.881e-03'
+  max: '1.573e-02'
+  mean: '4.815e-04'
+  min: '-7.385e-03'
   shape:
   - 64
-  sum: '2.194e-03'
+  sum: '3.081e-02'
 grads.network.layer1.0.conv1.weight:
   device: cuda:0
-  max: '3.228e-02'
-  mean: '-2.187e-04'
-  min: '-3.009e-02'
+  max: '3.727e-02'
+  mean: '4.84e-05'
+  min: '-2.909e-02'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '-8.063e+00'
+  sum: '1.784e+00'
 grads.network.layer1.0.conv2.weight:
   device: cuda:0
-  max: '2.011e-02'
-  mean: '-8.082e-05'
-  min: '-2.321e-02'
+  max: '2.013e-02'
+  mean: '9.22e-05'
+  min: '-1.982e-02'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '-2.979e+00'
+  sum: '3.399e+00'
 grads.network.layer1.1.bn1.bias:
   device: cuda:0
-  max: '8.757e-03'
-  mean: '3.335e-04'
-  min: '-8.009e-03'
+  max: '5.703e-03'
+  mean: '-5.734e-04'
+  min: '-8.499e-03'
   shape:
   - 64
-  sum: '2.134e-02'
+  sum: '-3.67e-02'
 grads.network.layer1.1.bn1.weight:
   device: cuda:0
-  max: '1.031e-02'
-  mean: '-1.251e-09'
-  min: '-8.325e-03'
+  max: '9.981e-03'
+  mean: '-4.860e-09'
+  min: '-8.737e-03'
   shape:
   - 64
-  sum: '-8.009e-08'
+  sum: '-3.111e-07'
 grads.network.layer1.1.bn2.bias:
   device: cuda:0
-  max: '3.688e-03'
-  mean: '-1.159e-04'
-  min: '-3.878e-03'
+  max: '4.094e-03'
+  mean: '-1.796e-04'
+  min: '-4.228e-03'
   shape:
   - 64
-  sum: '-7.419e-03'
+  sum: '-1.15e-02'
 grads.network.layer1.1.bn2.weight:
   device: cuda:0
-  max: '7.533e-03'
-  mean: '-1.319e-04'
-  min: '-1.042e-02'
+  max: '1.106e-02'
+  mean: '-3.951e-04'
+  min: '-8.18e-03'
   shape:
   - 64
-  sum: '-8.443e-03'
+  sum: '-2.529e-02'
 grads.network.layer1.1.conv1.weight:
   device: cuda:0
-  max: '1.682e-02'
-  mean: '7.859e-05'
-  min: '-1.756e-02'
+  max: '1.803e-02'
+  mean: '-7.176e-05'
+  min: '-1.731e-02'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '2.897e+00'
+  sum: '-2.645e+00'
 grads.network.layer1.1.conv2.weight:
   device: cuda:0
-  max: '1.164e-02'
-  mean: '-8.183e-05'
-  min: '-1.057e-02'
+  max: '1.123e-02'
+  mean: '-4.017e-06'
+  min: '-1.396e-02'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '-3.017e+00'
+  sum: '-1.481e-01'
 grads.network.layer2.0.bn1.bias:
   device: cuda:0
-  max: '6.346e-03'
-  mean: '3.467e-04'
-  min: '-5.223e-03'
+  max: '4.642e-03'
+  mean: '1.543e-04'
+  min: '-5.930e-03'
   shape:
   - 128
-  sum: '4.438e-02'
+  sum: '1.975e-02'
 grads.network.layer2.0.bn1.weight:
   device: cuda:0
-  max: '4.709e-03'
-  mean: '8.731e-11'
-  min: '-5.212e-03'
+  max: '4.549e-03'
+  mean: '-2.889e-09'
+  min: '-6.023e-03'
   shape:
   - 128
-  sum: '1.118e-08'
+  sum: '-3.697e-07'
 grads.network.layer2.0.bn2.bias:
   device: cuda:0
-  max: '4.109e-03'
-  mean: '1.036e-04'
-  min: '-5.165e-03'
+  max: '3.817e-03'
+  mean: '-2.366e-05'
+  min: '-3.840e-03'
   shape:
   - 128
-  sum: '1.326e-02'
+  sum: '-3.029e-03'
 grads.network.layer2.0.bn2.weight:
   device: cuda:0
-  max: '7.476e-03'
-  mean: '-1.799e-05'
-  min: '-5.677e-03'
+  max: '5.694e-03'
+  mean: '-9.502e-05'
+  min: '-5.515e-03'
   shape:
   - 128
-  sum: '-2.302e-03'
+  sum: '-1.216e-02'
 grads.network.layer2.0.conv1.weight:
   device: cuda:0
-  max: '1.684e-02'
-  mean: '-1.249e-04'
-  min: '-1.531e-02'
+  max: '1.456e-02'
+  mean: '2.676e-05'
+  min: '-1.177e-02'
   shape:
   - 128
   - 64
   - 3
   - 3
-  sum: '-9.211e+00'
+  sum: '1.973e+00'
 grads.network.layer2.0.conv2.weight:
   device: cuda:0
-  max: '9.979e-03'
-  mean: '-4.225e-05'
-  min: '-9.486e-03'
+  max: '8.337e-03'
+  mean: '-3.767e-05'
+  min: '-9.125e-03'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '-6.229e+00'
+  sum: '-5.555e+00'
 grads.network.layer2.0.downsample.0.weight:
   device: cuda:0
-  max: '1.095e-02'
-  mean: '-1.596e-04'
-  min: '-1.44e-02'
+  max: '9.921e-03'
+  mean: '-9.705e-05'
+  min: '-1.303e-02'
   shape:
   - 128
   - 64
   - 1
   - 1
-  sum: '-1.307e+00'
+  sum: '-7.950e-01'
 grads.network.layer2.0.downsample.1.bias:
   device: cuda:0
-  max: '4.109e-03'
-  mean: '1.036e-04'
-  min: '-5.165e-03'
+  max: '3.817e-03'
+  mean: '-2.366e-05'
+  min: '-3.840e-03'
   shape:
   - 128
-  sum: '1.326e-02'
+  sum: '-3.029e-03'
 grads.network.layer2.0.downsample.1.weight:
   device: cuda:0
-  max: '5.643e-03'
-  mean: '-9.116e-05'
-  min: '-5.724e-03'
+  max: '6.796e-03'
+  mean: '1.332e-04'
+  min: '-4.764e-03'
   shape:
   - 128
-  sum: '-1.167e-02'
+  sum: '1.705e-02'
 grads.network.layer2.1.bn1.bias:
   device: cuda:0
-  max: '3.875e-03'
-  mean: '2.269e-04'
-  min: '-3.296e-03'
+  max: '4.862e-03'
+  mean: '7.704e-05'
+  min: '-3.708e-03'
   shape:
   - 128
-  sum: '2.904e-02'
+  sum: '9.861e-03'
 grads.network.layer2.1.bn1.weight:
   device: cuda:0
-  max: '3.931e-03'
-  mean: '1.222e-09'
-  min: '-5.433e-03'
+  max: '5.664e-03'
+  mean: '-1.659e-09'
+  min: '-6.275e-03'
   shape:
   - 128
-  sum: '1.565e-07'
+  sum: '-2.123e-07'
 grads.network.layer2.1.bn2.bias:
   device: cuda:0
-  max: '3.029e-03'
-  mean: '1.229e-04'
-  min: '-2.608e-03'
+  max: '2.931e-03'
+  mean: '9.268e-05'
+  min: '-3.275e-03'
   shape:
   - 128
-  sum: '1.574e-02'
+  sum: '1.186e-02'
 grads.network.layer2.1.bn2.weight:
   device: cuda:0
-  max: '4.324e-03'
-  mean: '1.091e-04'
-  min: '-4.632e-03'
+  max: '3.809e-03'
+  mean: '-3.820e-05'
+  min: '-3.601e-03'
   shape:
   - 128
-  sum: '1.397e-02'
+  sum: '-4.89e-03'
 grads.network.layer2.1.conv1.weight:
   device: cuda:0
-  max: '8.457e-03'
-  mean: '-2.224e-05'
-  min: '-8.334e-03'
+  max: '8.135e-03'
+  mean: '-4.213e-06'
+  min: '-8.613e-03'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '-3.279e+00'
+  sum: '-6.212e-01'
 grads.network.layer2.1.conv2.weight:
   device: cuda:0
-  max: '6.936e-03'
-  mean: '-2.779e-05'
-  min: '-6.811e-03'
+  max: '6.837e-03'
+  mean: '-2.916e-05'
+  min: '-8.253e-03'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '-4.098e+00'
+  sum: '-4.300e+00'
 grads.network.layer3.0.bn1.bias:
   device: cuda:0
-  max: '2.770e-03'
-  mean: '5.8e-05'
-  min: '-3.176e-03'
+  max: '2.39e-03'
+  mean: '-2.179e-05'
+  min: '-2.675e-03'
   shape:
   - 256
-  sum: '1.485e-02'
+  sum: '-5.578e-03'
 grads.network.layer3.0.bn1.weight:
   device: cuda:0
-  max: '4.501e-03'
-  mean: '-1.965e-09'
-  min: '-3.247e-03'
+  max: '3.958e-03'
+  mean: '-3.711e-10'
+  min: '-3.378e-03'
   shape:
   - 256
-  sum: '-5.029e-07'
+  sum: '-9.499e-08'
 grads.network.layer3.0.bn2.bias:
   device: cuda:0
-  max: '2.85e-03'
-  mean: '2.536e-05'
-  min: '-3.149e-03'
+  max: '2.351e-03'
+  mean: '9.29e-06'
+  min: '-2.234e-03'
   shape:
   - 256
-  sum: '6.493e-03'
+  sum: '2.378e-03'
 grads.network.layer3.0.bn2.weight:
   device: cuda:0
-  max: '3.689e-03'
-  mean: '-1.113e-04'
-  min: '-3.318e-03'
+  max: '2.677e-03'
+  mean: '-6.531e-06'
+  min: '-3.361e-03'
   shape:
   - 256
-  sum: '-2.850e-02'
+  sum: '-1.672e-03'
 grads.network.layer3.0.conv1.weight:
   device: cuda:0
-  max: '8.373e-03'
-  mean: '1.589e-06'
-  min: '-8.216e-03'
+  max: '8.356e-03'
+  mean: '-1.346e-05'
+  min: '-7.572e-03'
   shape:
   - 256
   - 128
   - 3
   - 3
-  sum: '4.685e-01'
+  sum: '-3.969e+00'
 grads.network.layer3.0.conv2.weight:
   device: cuda:0
-  max: '7.279e-03'
-  mean: '3.597e-07'
-  min: '-6.876e-03'
+  max: '4.846e-03'
+  mean: '8.220e-06'
+  min: '-6.097e-03'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '2.122e-01'
+  sum: '4.849e+00'
 grads.network.layer3.0.downsample.0.weight:
   device: cuda:0
-  max: '7.642e-03'
-  mean: '7.352e-06'
-  min: '-6.323e-03'
+  max: '6.926e-03'
+  mean: '2.394e-05'
+  min: '-5.875e-03'
   shape:
   - 256
   - 128
   - 1
   - 1
-  sum: '2.409e-01'
+  sum: '7.844e-01'
 grads.network.layer3.0.downsample.1.bias:
   device: cuda:0
-  max: '2.85e-03'
-  mean: '2.536e-05'
-  min: '-3.149e-03'
+  max: '2.351e-03'
+  mean: '9.29e-06'
+  min: '-2.234e-03'
   shape:
   - 256
-  sum: '6.493e-03'
+  sum: '2.378e-03'
 grads.network.layer3.0.downsample.1.weight:
   device: cuda:0
-  max: '3.721e-03'
-  mean: '1.250e-04'
-  min: '-3.504e-03'
+  max: '3.362e-03'
+  mean: '4.264e-05'
+  min: '-2.955e-03'
   shape:
   - 256
-  sum: '3.201e-02'
+  sum: '1.092e-02'
 grads.network.layer3.1.bn1.bias:
   device: cuda:0
-  max: '2.634e-03'
-  mean: '3.564e-05'
-  min: '-2.17e-03'
+  max: '1.942e-03'
+  mean: '-9.510e-06'
+  min: '-2.224e-03'
   shape:
   - 256
-  sum: '9.124e-03'
+  sum: '-2.435e-03'
 grads.network.layer3.1.bn1.weight:
   device: cuda:0
-  max: '2.518e-03'
-  mean: '1.983e-10'
-  min: '-2.539e-03'
+  max: '2.689e-03'
+  mean: '-5.948e-10'
+  min: '-3.468e-03'
   shape:
   - 256
-  sum: '5.076e-08'
+  sum: '-1.523e-07'
 grads.network.layer3.1.bn2.bias:
   device: cuda:0
-  max: '2.024e-03'
-  mean: '6.733e-05'
-  min: '-1.777e-03'
+  max: '1.634e-03'
+  mean: '2.694e-05'
+  min: '-1.504e-03'
   shape:
   - 256
-  sum: '1.724e-02'
+  sum: '6.896e-03'
 grads.network.layer3.1.bn2.weight:
   device: cuda:0
-  max: '2.737e-03'
-  mean: '-1.37e-05'
-  min: '-2.669e-03'
+  max: '2.593e-03'
+  mean: '-3.611e-05'
+  min: '-3.369e-03'
   shape:
   - 256
-  sum: '-3.507e-03'
+  sum: '-9.244e-03'
 grads.network.layer3.1.conv1.weight:
   device: cuda:0
-  max: '5.457e-03'
-  mean: '-1.498e-06'
-  min: '-5.48e-03'
+  max: '5.157e-03'
+  mean: '8.517e-06'
+  min: '-4.620e-03'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-8.836e-01'
+  sum: '5.024e+00'
 grads.network.layer3.1.conv2.weight:
   device: cuda:0
-  max: '4.436e-03'
-  mean: '7.578e-07'
-  min: '-4.453e-03'
+  max: '4.516e-03'
+  mean: '-1.377e-05'
+  min: '-3.671e-03'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '4.469e-01'
+  sum: '-8.122e+00'
 grads.network.layer4.0.bn1.bias:
   device: cuda:0
-  max: '1.529e-03'
-  mean: '4.731e-05'
-  min: '-1.600e-03'
+  max: '1.389e-03'
+  mean: '2.127e-05'
+  min: '-1.66e-03'
   shape:
   - 512
-  sum: '2.422e-02'
+  sum: '1.089e-02'
 grads.network.layer4.0.bn1.weight:
   device: cuda:0
-  max: '2.836e-03'
-  mean: '3.382e-09'
-  min: '-1.948e-03'
+  max: '3.501e-03'
+  mean: '2.910e-09'
+  min: '-3.476e-03'
   shape:
   - 512
-  sum: '1.731e-06'
+  sum: '1.490e-06'
 grads.network.layer4.0.bn2.bias:
   device: cuda:0
-  max: '4.572e-03'
-  mean: '2.561e-04'
-  min: '-3.552e-03'
+  max: '4.854e-03'
+  mean: '2.704e-04'
+  min: '-3.529e-03'
   shape:
   - 512
-  sum: '1.311e-01'
+  sum: '1.385e-01'
 grads.network.layer4.0.bn2.weight:
   device: cuda:0
-  max: '4.103e-03'
-  mean: '2.118e-04'
-  min: '-2.870e-03'
+  max: '3.923e-03'
+  mean: '1.858e-04'
+  min: '-2.593e-03'
   shape:
   - 512
-  sum: '1.084e-01'
+  sum: '9.515e-02'
 grads.network.layer4.0.conv1.weight:
   device: cuda:0
-  max: '5.52e-03'
-  mean: '-1.319e-05'
-  min: '-5.398e-03'
+  max: '5.933e-03'
+  mean: '-4.272e-06'
+  min: '-5.908e-03'
   shape:
   - 512
   - 256
   - 3
   - 3
-  sum: '-1.556e+01'
+  sum: '-5.039e+00'
 grads.network.layer4.0.conv2.weight:
   device: cuda:0
-  max: '3.6e-03'
-  mean: '-4.087e-06'
-  min: '-4.384e-03'
+  max: '3.158e-03'
+  mean: '2.135e-06'
+  min: '-3.562e-03'
   shape:
   - 512
   - 512
   - 3
   - 3
-  sum: '-9.643e+00'
+  sum: '5.037e+00'
 grads.network.layer4.0.downsample.0.weight:
   device: cuda:0
-  max: '4.390e-03'
-  mean: '-2.207e-06'
-  min: '-5.205e-03'
+  max: '4.447e-03'
+  mean: '1.095e-05'
+  min: '-4.228e-03'
   shape:
   - 512
   - 256
   - 1
   - 1
-  sum: '-2.893e-01'
+  sum: '1.436e+00'
 grads.network.layer4.0.downsample.1.bias:
   device: cuda:0
-  max: '4.572e-03'
-  mean: '2.561e-04'
-  min: '-3.552e-03'
+  max: '4.854e-03'
+  mean: '2.704e-04'
+  min: '-3.529e-03'
   shape:
   - 512
-  sum: '1.311e-01'
+  sum: '1.385e-01'
 grads.network.layer4.0.downsample.1.weight:
   device: cuda:0
-  max: '3.626e-03'
-  mean: '1.351e-04'
-  min: '-3.259e-03'
+  max: '2.905e-03'
+  mean: '1.773e-04'
+  min: '-3.313e-03'
   shape:
   - 512
-  sum: '6.917e-02'
+  sum: '9.076e-02'
 grads.network.layer4.1.bn1.bias:
   device: cuda:0
-  max: '1.327e-03'
-  mean: '1.918e-05'
-  min: '-1.29e-03'
+  max: '1.308e-03'
+  mean: '1.466e-05'
+  min: '-1.400e-03'
   shape:
   - 512
-  sum: '9.818e-03'
+  sum: '7.505e-03'
 grads.network.layer4.1.bn1.weight:
   device: cuda:0
-  max: '2.764e-03'
-  mean: '3.335e-09'
-  min: '-2.679e-03'
+  max: '2.31e-03'
+  mean: '2.845e-09'
+  min: '-2.817e-03'
   shape:
   - 512
-  sum: '1.707e-06'
+  sum: '1.457e-06'
 grads.network.layer4.1.bn2.bias:
   device: cuda:0
-  max: '7.656e-03'
-  mean: '4.169e-04'
-  min: '-5.189e-03'
+  max: '7.246e-03'
+  mean: '4.285e-04'
+  min: '-4.605e-03'
   shape:
   - 512
-  sum: '2.134e-01'
+  sum: '2.194e-01'
 grads.network.layer4.1.bn2.weight:
   device: cuda:0
-  max: '3.609e-03'
-  mean: '2.029e-04'
-  min: '-3.125e-03'
+  max: '3.809e-03'
+  mean: '1.852e-04'
+  min: '-3.260e-03'
   shape:
   - 512
-  sum: '1.039e-01'
+  sum: '9.484e-02'
 grads.network.layer4.1.conv1.weight:
   device: cuda:0
-  max: '4.400e-03'
-  mean: '-9.705e-06'
-  min: '-3.475e-03'
+  max: '3.772e-03'
+  mean: '-4.186e-06'
+  min: '-3.472e-03'
   shape:
   - 512
   - 512
   - 3
   - 3
-  sum: '-2.29e+01'
+  sum: '-9.876e+00'
 grads.network.layer4.1.conv2.weight:
   device: cuda:0
-  max: '3.91e-03'
-  mean: '1.074e-05'
-  min: '-2.999e-03'
+  max: '3.217e-03'
+  mean: '6.716e-06'
+  min: '-3.656e-03'
   shape:
   - 512
   - 512
   - 3
   - 3
-  sum: '2.535e+01'
+  sum: '1.584e+01'
 outputs.logits:
   device: cuda:0
-  max: '2.934e+00'
-  mean: '-8.071e-04'
-  min: '-2.896e+00'
+  max: '2.513e+00'
+  mean: '-5.438e-04'
+  min: '-2.572e+00'
   shape:
   - 64
   - 1000
-  sum: '-5.165e+01'
+  sum: '-3.480e+01'
 outputs.loss:
   device: cuda:0
-  max: '7.073e+00'
-  mean: '7.073e+00'
-  min: '7.073e+00'
+  max: '7.074e+00'
+  mean: '7.074e+00'
+  min: '7.074e+00'
   shape: []
-  sum: '7.073e+00'
+  sum: '7.074e+00'
 outputs.y:
   device: cuda:0
   max: 988
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet50_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet50_cifar10_image_classifier.yaml
index 3fafcadf..f4a696f5 100644
--- a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet50_cifar10_image_classifier.yaml
+++ b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet50_cifar10_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.126e+00'
-  mean: '-6.179e-03'
+  mean: '6.869e-03'
   min: '-1.989e+00'
   shape:
   - 128
   - 3
   - 32
   - 32
-  sum: '-2.43e+03'
+  sum: '2.701e+03'
 batch.1:
   device: cuda:0
   max: 9
@@ -19,1468 +19,1468 @@ batch.1:
   sum: 583
 grads.network.bn1.bias:
   device: cuda:0
-  max: '9.205e-01'
-  mean: '4.814e-02'
-  min: '-1.080e+00'
+  max: '1.228e+00'
+  mean: '4.070e-02'
+  min: '-6.757e-01'
   shape:
   - 64
-  sum: '3.081e+00'
+  sum: '2.605e+00'
 grads.network.bn1.weight:
   device: cuda:0
-  max: '1.441e+00'
-  mean: '3.662e-06'
-  min: '-1.737e+00'
+  max: '2.101e+00'
+  mean: '1.214e-06'
+  min: '-1.619e+00'
   shape:
   - 64
-  sum: '2.344e-04'
+  sum: '7.772e-05'
 grads.network.conv1.weight:
   device: cuda:0
-  max: '1.895e+01'
-  mean: '-8.353e-03'
-  min: '-1.422e+01'
+  max: '1.518e+01'
+  mean: '3.971e-02'
+  min: '-1.728e+01'
   shape:
   - 64
   - 3
   - 7
   - 7
-  sum: '-7.858e+01'
+  sum: '3.736e+02'
 grads.network.fc.bias:
   device: cuda:0
-  max: '1.341e-01'
-  mean: '1.490e-09'
-  min: '-6.681e-02'
+  max: '1.344e-01'
+  mean: '0.e+00'
+  min: '-6.531e-02'
   shape:
   - 10
-  sum: '1.490e-08'
+  sum: '0.e+00'
 grads.network.fc.weight:
   device: cuda:0
-  max: '3.777e-01'
-  mean: '5.101e-10'
-  min: '-2.029e-01'
+  max: '3.3e-01'
+  mean: '-1.094e-09'
+  min: '-2.508e-01'
   shape:
   - 10
   - 2048
-  sum: '1.045e-05'
+  sum: '-2.24e-05'
 grads.network.layer1.0.bn1.bias:
   device: cuda:0
-  max: '8.082e-01'
-  mean: '1.893e-02'
-  min: '-8.557e-01'
+  max: '1.223e+00'
+  mean: '1.200e-01'
+  min: '-9.51e-01'
   shape:
   - 64
-  sum: '1.211e+00'
+  sum: '7.682e+00'
 grads.network.layer1.0.bn1.weight:
   device: cuda:0
-  max: '7.796e-01'
-  mean: '-1.248e-07'
-  min: '-9.923e-01'
+  max: '1.201e+00'
+  mean: '-7.078e-07'
+  min: '-1.471e+00'
   shape:
   - 64
-  sum: '-7.987e-06'
+  sum: '-4.53e-05'
 grads.network.layer1.0.bn2.bias:
   device: cuda:0
-  max: '6.138e-01'
-  mean: '-3.147e-02'
-  min: '-7.454e-01'
+  max: '8.938e-01'
+  mean: '-8.675e-03'
+  min: '-6.429e-01'
   shape:
   - 64
-  sum: '-2.014e+00'
+  sum: '-5.552e-01'
 grads.network.layer1.0.bn2.weight:
   device: cuda:0
-  max: '8.566e-01'
-  mean: '-4.075e-06'
-  min: '-8.725e-01'
+  max: '1.309e+00'
+  mean: '-9.313e-08'
+  min: '-8.195e-01'
   shape:
   - 64
-  sum: '-2.608e-04'
+  sum: '-5.960e-06'
 grads.network.layer1.0.bn3.bias:
   device: cuda:0
-  max: '4.064e-01'
-  mean: '-1.042e-04'
-  min: '-4.231e-01'
+  max: '3.648e-01'
+  mean: '-1.964e-03'
+  min: '-3.354e-01'
   shape:
   - 256
-  sum: '-2.667e-02'
+  sum: '-5.029e-01'
 grads.network.layer1.0.bn3.weight:
   device: cuda:0
-  max: '5.445e-01'
-  mean: '-1.607e-02'
-  min: '-5.301e-01'
+  max: '4.922e-01'
+  mean: '7.187e-04'
+  min: '-4.949e-01'
   shape:
   - 256
-  sum: '-4.115e+00'
+  sum: '1.84e-01'
 grads.network.layer1.0.conv1.weight:
   device: cuda:0
-  max: '1.995e+00'
-  mean: '5.037e-03'
-  min: '-2.531e+00'
+  max: '2.273e+00'
+  mean: '1.648e-02'
+  min: '-2.233e+00'
   shape:
   - 64
   - 64
   - 1
   - 1
-  sum: '2.063e+01'
+  sum: '6.749e+01'
 grads.network.layer1.0.conv2.weight:
   device: cuda:0
-  max: '1.94e+00'
-  mean: '9.205e-03'
-  min: '-1.562e+00'
+  max: '1.623e+00'
+  mean: '-3.302e-03'
+  min: '-2.030e+00'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '3.393e+02'
+  sum: '-1.217e+02'
 grads.network.layer1.0.conv3.weight:
   device: cuda:0
-  max: '1.516e+00'
-  mean: '1.730e-03'
-  min: '-1.296e+00'
+  max: '1.32e+00'
+  mean: '2.987e-03'
+  min: '-1.210e+00'
   shape:
   - 256
   - 64
   - 1
   - 1
-  sum: '2.835e+01'
+  sum: '4.894e+01'
 grads.network.layer1.0.downsample.0.weight:
   device: cuda:0
-  max: '1.394e+00'
-  mean: '6.997e-03'
-  min: '-1.394e+00'
+  max: '1.933e+00'
+  mean: '1.191e-02'
+  min: '-1.661e+00'
   shape:
   - 256
   - 64
   - 1
   - 1
-  sum: '1.146e+02'
+  sum: '1.952e+02'
 grads.network.layer1.0.downsample.1.bias:
   device: cuda:0
-  max: '4.064e-01'
-  mean: '-1.042e-04'
-  min: '-4.231e-01'
+  max: '3.648e-01'
+  mean: '-1.964e-03'
+  min: '-3.354e-01'
   shape:
   - 256
-  sum: '-2.667e-02'
+  sum: '-5.029e-01'
 grads.network.layer1.0.downsample.1.weight:
   device: cuda:0
-  max: '7.517e-01'
-  mean: '1.179e-02'
-  min: '-4.804e-01'
+  max: '5.088e-01'
+  mean: '1.033e-03'
+  min: '-4.753e-01'
   shape:
   - 256
-  sum: '3.017e+00'
+  sum: '2.645e-01'
 grads.network.layer1.1.bn1.bias:
   device: cuda:0
-  max: '5.352e-01'
-  mean: '-5.139e-03'
-  min: '-6.301e-01'
+  max: '7.733e-01'
+  mean: '2.665e-02'
+  min: '-7.12e-01'
   shape:
   - 64
-  sum: '-3.289e-01'
+  sum: '1.706e+00'
 grads.network.layer1.1.bn1.weight:
   device: cuda:0
-  max: '7.305e-01'
-  mean: '-1.322e-07'
-  min: '-6.086e-01'
+  max: '6.744e-01'
+  mean: '5.588e-08'
+  min: '-9.565e-01'
   shape:
   - 64
-  sum: '-8.464e-06'
+  sum: '3.576e-06'
 grads.network.layer1.1.bn2.bias:
   device: cuda:0
-  max: '6.326e-01'
-  mean: '-2.056e-03'
-  min: '-4.814e-01'
+  max: '6.522e-01'
+  mean: '1.98e-02'
+  min: '-3.258e-01'
   shape:
   - 64
-  sum: '-1.316e-01'
+  sum: '1.267e+00'
 grads.network.layer1.1.bn2.weight:
   device: cuda:0
-  max: '7.657e-01'
-  mean: '2.328e-08'
-  min: '-5.989e-01'
+  max: '7.653e-01'
+  mean: '-6.733e-07'
+  min: '-6.189e-01'
   shape:
   - 64
-  sum: '1.490e-06'
+  sum: '-4.309e-05'
 grads.network.layer1.1.bn3.bias:
   device: cuda:0
-  max: '2.399e-01'
-  mean: '5.205e-03'
-  min: '-1.858e-01'
+  max: '2.149e-01'
+  mean: '1.430e-03'
+  min: '-2.273e-01'
   shape:
   - 256
-  sum: '1.333e+00'
+  sum: '3.661e-01'
 grads.network.layer1.1.bn3.weight:
   device: cuda:0
-  max: '3.889e-01'
-  mean: '2.229e-03'
-  min: '-3.122e-01'
+  max: '2.567e-01'
+  mean: '-3.546e-03'
+  min: '-4.186e-01'
   shape:
   - 256
-  sum: '5.706e-01'
+  sum: '-9.079e-01'
 grads.network.layer1.1.conv1.weight:
   device: cuda:0
-  max: '6.541e-01'
-  mean: '6.722e-04'
-  min: '-6.24e-01'
+  max: '6.135e-01'
+  mean: '3.297e-03'
+  min: '-5.673e-01'
   shape:
   - 64
   - 256
   - 1
   - 1
-  sum: '1.101e+01'
+  sum: '5.401e+01'
 grads.network.layer1.1.conv2.weight:
   device: cuda:0
-  max: '1.279e+00'
-  mean: '6.102e-03'
-  min: '-1.024e+00'
+  max: '1.274e+00'
+  mean: '-3.424e-03'
+  min: '-1.103e+00'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '2.249e+02'
+  sum: '-1.262e+02'
 grads.network.layer1.1.conv3.weight:
   device: cuda:0
-  max: '9.491e-01'
-  mean: '2.511e-03'
-  min: '-9.537e-01'
+  max: '8.389e-01'
+  mean: '-5.015e-04'
+  min: '-7.878e-01'
   shape:
   - 256
   - 64
   - 1
   - 1
-  sum: '4.114e+01'
+  sum: '-8.216e+00'
 grads.network.layer1.2.bn1.bias:
   device: cuda:0
-  max: '4.21e-01'
-  mean: '-1.548e-02'
-  min: '-4.326e-01'
+  max: '4.553e-01'
+  mean: '1.777e-02'
+  min: '-3.004e-01'
   shape:
   - 64
-  sum: '-9.907e-01'
+  sum: '1.138e+00'
 grads.network.layer1.2.bn1.weight:
   device: cuda:0
-  max: '5.188e-01'
-  mean: '1.397e-08'
-  min: '-3.354e-01'
+  max: '4.624e-01'
+  mean: '-6.519e-09'
+  min: '-4.765e-01'
   shape:
   - 64
-  sum: '8.941e-07'
+  sum: '-4.172e-07'
 grads.network.layer1.2.bn2.bias:
   device: cuda:0
-  max: '4.175e-01'
-  mean: '-7.536e-03'
-  min: '-3.544e-01'
+  max: '3.886e-01'
+  mean: '-1.352e-02'
+  min: '-3.382e-01'
   shape:
   - 64
-  sum: '-4.823e-01'
+  sum: '-8.65e-01'
 grads.network.layer1.2.bn2.weight:
   device: cuda:0
-  max: '2.97e-01'
-  mean: '5.048e-07'
-  min: '-3.822e-01'
+  max: '3.698e-01'
+  mean: '7.562e-07'
+  min: '-3.949e-01'
   shape:
   - 64
-  sum: '3.231e-05'
+  sum: '4.84e-05'
 grads.network.layer1.2.bn3.bias:
   device: cuda:0
-  max: '1.238e-01'
-  mean: '2.877e-03'
-  min: '-1.060e-01'
+  max: '1.177e-01'
+  mean: '4.226e-05'
+  min: '-1.336e-01'
   shape:
   - 256
-  sum: '7.366e-01'
+  sum: '1.082e-02'
 grads.network.layer1.2.bn3.weight:
   device: cuda:0
-  max: '2.316e-01'
-  mean: '2.059e-03'
-  min: '-2.506e-01'
+  max: '2.695e-01'
+  mean: '1.794e-03'
+  min: '-2.158e-01'
   shape:
   - 256
-  sum: '5.272e-01'
+  sum: '4.594e-01'
 grads.network.layer1.2.conv1.weight:
   device: cuda:0
-  max: '3.633e-01'
-  mean: '3.658e-03'
-  min: '-4.331e-01'
+  max: '4.299e-01'
+  mean: '7.214e-04'
+  min: '-4.234e-01'
   shape:
   - 64
   - 256
   - 1
   - 1
-  sum: '5.993e+01'
+  sum: '1.182e+01'
 grads.network.layer1.2.conv2.weight:
   device: cuda:0
-  max: '6.992e-01'
-  mean: '2.97e-03'
-  min: '-7.175e-01'
+  max: '7.052e-01'
+  mean: '-1.495e-03'
+  min: '-9.052e-01'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '1.095e+02'
+  sum: '-5.512e+01'
 grads.network.layer1.2.conv3.weight:
   device: cuda:0
-  max: '5.388e-01'
-  mean: '-1.901e-04'
-  min: '-6.321e-01'
+  max: '5.433e-01'
+  mean: '-1.917e-03'
+  min: '-6.151e-01'
   shape:
   - 256
   - 64
   - 1
   - 1
-  sum: '-3.115e+00'
+  sum: '-3.140e+01'
 grads.network.layer2.0.bn1.bias:
   device: cuda:0
-  max: '2.419e-01'
-  mean: '-5.441e-03'
-  min: '-2.731e-01'
+  max: '2.047e-01'
+  mean: '2.724e-03'
+  min: '-3.359e-01'
   shape:
   - 128
-  sum: '-6.964e-01'
+  sum: '3.487e-01'
 grads.network.layer2.0.bn1.weight:
   device: cuda:0
-  max: '3.249e-01'
-  mean: '2.258e-08'
-  min: '-2.792e-01'
+  max: '3.621e-01'
+  mean: '4.773e-08'
+  min: '-3.823e-01'
   shape:
   - 128
-  sum: '2.891e-06'
+  sum: '6.109e-06'
 grads.network.layer2.0.bn2.bias:
   device: cuda:0
-  max: '1.974e-01'
-  mean: '-7.017e-03'
-  min: '-2.037e-01'
+  max: '2.486e-01'
+  mean: '3.34e-03'
+  min: '-1.732e-01'
   shape:
   - 128
-  sum: '-8.981e-01'
+  sum: '4.275e-01'
 grads.network.layer2.0.bn2.weight:
   device: cuda:0
-  max: '3.613e-01'
-  mean: '6.775e-08'
-  min: '-2.713e-01'
+  max: '3.521e-01'
+  mean: '-6.268e-07'
+  min: '-2.717e-01'
   shape:
   - 128
-  sum: '8.672e-06'
+  sum: '-8.023e-05'
 grads.network.layer2.0.bn3.bias:
   device: cuda:0
-  max: '1.091e-01'
-  mean: '6.263e-04'
-  min: '-1.059e-01'
+  max: '1.078e-01'
+  mean: '7.493e-04'
+  min: '-9.564e-02'
   shape:
   - 512
-  sum: '3.207e-01'
+  sum: '3.836e-01'
 grads.network.layer2.0.bn3.weight:
   device: cuda:0
-  max: '1.658e-01'
-  mean: '-1.899e-04'
-  min: '-1.353e-01'
+  max: '1.234e-01'
+  mean: '-3.09e-03'
+  min: '-1.452e-01'
   shape:
   - 512
-  sum: '-9.725e-02'
+  sum: '-1.582e+00'
 grads.network.layer2.0.conv1.weight:
   device: cuda:0
-  max: '3.953e-01'
-  mean: '1.031e-03'
-  min: '-3.708e-01'
+  max: '3.292e-01'
+  mean: '-1.771e-03'
+  min: '-3.914e-01'
   shape:
   - 128
   - 256
   - 1
   - 1
-  sum: '3.38e+01'
+  sum: '-5.803e+01'
 grads.network.layer2.0.conv2.weight:
   device: cuda:0
-  max: '4.388e-01'
-  mean: '1.736e-03'
-  min: '-4.009e-01'
+  max: '4.522e-01'
+  mean: '-2.27e-04'
+  min: '-4.315e-01'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '2.560e+02'
+  sum: '-3.347e+01'
 grads.network.layer2.0.conv3.weight:
   device: cuda:0
-  max: '3.455e-01'
-  mean: '8.466e-04'
-  min: '-3.519e-01'
+  max: '3.651e-01'
+  mean: '-6.347e-04'
+  min: '-3.352e-01'
   shape:
   - 512
   - 128
   - 1
   - 1
-  sum: '5.548e+01'
+  sum: '-4.16e+01'
 grads.network.layer2.0.downsample.0.weight:
   device: cuda:0
-  max: '2.479e-01'
-  mean: '3.199e-04'
-  min: '-2.569e-01'
+  max: '2.666e-01'
+  mean: '-6.974e-04'
+  min: '-3.029e-01'
   shape:
   - 512
   - 256
   - 1
   - 1
-  sum: '4.193e+01'
+  sum: '-9.141e+01'
 grads.network.layer2.0.downsample.1.bias:
   device: cuda:0
-  max: '1.091e-01'
-  mean: '6.263e-04'
-  min: '-1.059e-01'
+  max: '1.078e-01'
+  mean: '7.493e-04'
+  min: '-9.564e-02'
   shape:
   - 512
-  sum: '3.207e-01'
+  sum: '3.836e-01'
 grads.network.layer2.0.downsample.1.weight:
   device: cuda:0
-  max: '1.697e-01'
-  mean: '1.416e-03'
-  min: '-1.327e-01'
+  max: '1.272e-01'
+  mean: '3.33e-03'
+  min: '-1.349e-01'
   shape:
   - 512
-  sum: '7.250e-01'
+  sum: '1.705e+00'
 grads.network.layer2.1.bn1.bias:
   device: cuda:0
-  max: '1.482e-01'
-  mean: '-1.673e-03'
-  min: '-1.761e-01'
+  max: '2.474e-01'
+  mean: '9.371e-03'
+  min: '-2.691e-01'
   shape:
   - 128
-  sum: '-2.141e-01'
+  sum: '1.2e+00'
 grads.network.layer2.1.bn1.weight:
   device: cuda:0
-  max: '1.848e-01'
-  mean: '-3.888e-08'
-  min: '-2.179e-01'
+  max: '2.249e-01'
+  mean: '2.328e-08'
+  min: '-2.023e-01'
   shape:
   - 128
-  sum: '-4.977e-06'
+  sum: '2.980e-06'
 grads.network.layer2.1.bn2.bias:
   device: cuda:0
-  max: '1.764e-01'
-  mean: '5.389e-03'
-  min: '-1.466e-01'
+  max: '1.318e-01'
+  mean: '-5.615e-03'
+  min: '-1.250e-01'
   shape:
   - 128
-  sum: '6.898e-01'
+  sum: '-7.187e-01'
 grads.network.layer2.1.bn2.weight:
   device: cuda:0
-  max: '2.348e-01'
-  mean: '-1.404e-07'
-  min: '-2.435e-01'
+  max: '2.726e-01'
+  mean: '-1.118e-07'
+  min: '-2.006e-01'
   shape:
   - 128
-  sum: '-1.797e-05'
+  sum: '-1.431e-05'
 grads.network.layer2.1.bn3.bias:
   device: cuda:0
-  max: '8.049e-02'
-  mean: '-1.62e-04'
-  min: '-6.643e-02'
+  max: '7.954e-02'
+  mean: '1.591e-04'
+  min: '-7.013e-02'
   shape:
   - 512
-  sum: '-8.292e-02'
+  sum: '8.147e-02'
 grads.network.layer2.1.bn3.weight:
   device: cuda:0
-  max: '1.130e-01'
-  mean: '1.227e-04'
-  min: '-9.870e-02'
+  max: '9.909e-02'
+  mean: '-5.327e-04'
+  min: '-9.670e-02'
   shape:
   - 512
-  sum: '6.285e-02'
+  sum: '-2.727e-01'
 grads.network.layer2.1.conv1.weight:
   device: cuda:0
-  max: '2.100e-01'
-  mean: '-3.326e-04'
-  min: '-1.831e-01'
+  max: '1.659e-01'
+  mean: '-1.31e-03'
+  min: '-1.883e-01'
   shape:
   - 128
   - 512
   - 1
   - 1
-  sum: '-2.18e+01'
+  sum: '-8.583e+01'
 grads.network.layer2.1.conv2.weight:
   device: cuda:0
-  max: '3.447e-01'
-  mean: '-9.641e-04'
-  min: '-3.505e-01'
+  max: '4.02e-01'
+  mean: '-1.964e-03'
+  min: '-3.418e-01'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '-1.422e+02'
+  sum: '-2.896e+02'
 grads.network.layer2.1.conv3.weight:
   device: cuda:0
-  max: '2.356e-01'
-  mean: '-1.869e-04'
-  min: '-2.254e-01'
+  max: '2.92e-01'
+  mean: '-8.794e-05'
+  min: '-2.413e-01'
   shape:
   - 512
   - 128
   - 1
   - 1
-  sum: '-1.225e+01'
+  sum: '-5.763e+00'
 grads.network.layer2.2.bn1.bias:
   device: cuda:0
-  max: '1.512e-01'
-  mean: '-1.99e-03'
-  min: '-1.240e-01'
+  max: '1.601e-01'
+  mean: '4.066e-04'
+  min: '-1.432e-01'
   shape:
   - 128
-  sum: '-2.547e-01'
+  sum: '5.205e-02'
 grads.network.layer2.2.bn1.weight:
   device: cuda:0
-  max: '1.999e-01'
-  mean: '2.270e-08'
-  min: '-1.396e-01'
+  max: '1.646e-01'
+  mean: '-9.546e-09'
+  min: '-1.578e-01'
   shape:
   - 128
-  sum: '2.906e-06'
+  sum: '-1.222e-06'
 grads.network.layer2.2.bn2.bias:
   device: cuda:0
-  max: '1.029e-01'
-  mean: '-3.850e-04'
-  min: '-1.010e-01'
+  max: '1.319e-01'
+  mean: '-1.114e-03'
+  min: '-7.673e-02'
   shape:
   - 128
-  sum: '-4.928e-02'
+  sum: '-1.426e-01'
 grads.network.layer2.2.bn2.weight:
   device: cuda:0
-  max: '1.463e-01'
-  mean: '-1.162e-07'
-  min: '-1.46e-01'
+  max: '1.529e-01'
+  mean: '-9.686e-08'
+  min: '-9.693e-02'
   shape:
   - 128
-  sum: '-1.487e-05'
+  sum: '-1.24e-05'
 grads.network.layer2.2.bn3.bias:
   device: cuda:0
-  max: '4.505e-02'
-  mean: '-9.093e-05'
-  min: '-3.943e-02'
+  max: '3.599e-02'
+  mean: '3.512e-04'
+  min: '-3.906e-02'
   shape:
   - 512
-  sum: '-4.656e-02'
+  sum: '1.798e-01'
 grads.network.layer2.2.bn3.weight:
   device: cuda:0
-  max: '8.137e-02'
-  mean: '-4.692e-04'
-  min: '-6.764e-02'
+  max: '7.732e-02'
+  mean: '-2.086e-04'
+  min: '-7.521e-02'
   shape:
   - 512
-  sum: '-2.402e-01'
+  sum: '-1.068e-01'
 grads.network.layer2.2.conv1.weight:
   device: cuda:0
-  max: '1.230e-01'
-  mean: '2.737e-04'
-  min: '-1.255e-01'
+  max: '1.333e-01'
+  mean: '-5.114e-05'
+  min: '-1.223e-01'
   shape:
   - 128
   - 512
   - 1
   - 1
-  sum: '1.794e+01'
+  sum: '-3.351e+00'
 grads.network.layer2.2.conv2.weight:
   device: cuda:0
-  max: '2.359e-01'
-  mean: '4.964e-04'
-  min: '-2.379e-01'
+  max: '2.340e-01'
+  mean: '2.054e-05'
+  min: '-2.369e-01'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '7.32e+01'
+  sum: '3.028e+00'
 grads.network.layer2.2.conv3.weight:
   device: cuda:0
-  max: '1.738e-01'
-  mean: '4.385e-04'
-  min: '-1.777e-01'
+  max: '1.892e-01'
+  mean: '-2.206e-04'
+  min: '-1.804e-01'
   shape:
   - 512
   - 128
   - 1
   - 1
-  sum: '2.874e+01'
+  sum: '-1.446e+01'
 grads.network.layer2.3.bn1.bias:
   device: cuda:0
-  max: '1.279e-01'
-  mean: '6.022e-03'
-  min: '-8.782e-02'
+  max: '1.226e-01'
+  mean: '8.906e-04'
+  min: '-1.071e-01'
   shape:
   - 128
-  sum: '7.708e-01'
+  sum: '1.14e-01'
 grads.network.layer2.3.bn1.weight:
   device: cuda:0
-  max: '1.222e-01'
-  mean: '1.199e-08'
-  min: '-1.526e-01'
+  max: '1.952e-01'
+  mean: '-2.503e-08'
+  min: '-1.162e-01'
   shape:
   - 128
-  sum: '1.535e-06'
+  sum: '-3.204e-06'
 grads.network.layer2.3.bn2.bias:
   device: cuda:0
-  max: '9.101e-02'
-  mean: '-1.522e-03'
-  min: '-7.893e-02'
+  max: '9.551e-02'
+  mean: '2.768e-03'
+  min: '-8.721e-02'
   shape:
   - 128
-  sum: '-1.948e-01'
+  sum: '3.543e-01'
 grads.network.layer2.3.bn2.weight:
   device: cuda:0
-  max: '8.481e-02'
-  mean: '-1.932e-07'
-  min: '-8.458e-02'
+  max: '1.141e-01'
+  mean: '1.066e-07'
+  min: '-9.926e-02'
   shape:
   - 128
-  sum: '-2.474e-05'
+  sum: '1.365e-05'
 grads.network.layer2.3.bn3.bias:
   device: cuda:0
-  max: '2.302e-02'
-  mean: '1.906e-05'
-  min: '-3.022e-02'
+  max: '2.594e-02'
+  mean: '2.204e-04'
+  min: '-2.765e-02'
   shape:
   - 512
-  sum: '9.761e-03'
+  sum: '1.129e-01'
 grads.network.layer2.3.bn3.weight:
   device: cuda:0
-  max: '4.318e-02'
-  mean: '-8.797e-04'
-  min: '-4.599e-02'
+  max: '4.800e-02'
+  mean: '5.013e-04'
+  min: '-4.687e-02'
   shape:
   - 512
-  sum: '-4.504e-01'
+  sum: '2.567e-01'
 grads.network.layer2.3.conv1.weight:
   device: cuda:0
-  max: '8.230e-02'
-  mean: '-3.507e-04'
-  min: '-9.358e-02'
+  max: '9.579e-02'
+  mean: '-2.184e-04'
+  min: '-9.235e-02'
   shape:
   - 128
   - 512
   - 1
   - 1
-  sum: '-2.298e+01'
+  sum: '-1.431e+01'
 grads.network.layer2.3.conv2.weight:
   device: cuda:0
-  max: '1.666e-01'
-  mean: '8.926e-04'
-  min: '-1.69e-01'
+  max: '1.748e-01'
+  mean: '-6.976e-04'
+  min: '-1.815e-01'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '1.316e+02'
+  sum: '-1.029e+02'
 grads.network.layer2.3.conv3.weight:
   device: cuda:0
-  max: '1.444e-01'
-  mean: '1.829e-04'
-  min: '-1.152e-01'
+  max: '1.168e-01'
+  mean: '-2.776e-04'
+  min: '-1.226e-01'
   shape:
   - 512
   - 128
   - 1
   - 1
-  sum: '1.199e+01'
+  sum: '-1.819e+01'
 grads.network.layer3.0.bn1.bias:
   device: cuda:0
-  max: '6.992e-02'
-  mean: '1.721e-03'
-  min: '-8.225e-02'
+  max: '6.05e-02'
+  mean: '4.034e-04'
+  min: '-8.745e-02'
   shape:
   - 256
-  sum: '4.405e-01'
+  sum: '1.033e-01'
 grads.network.layer3.0.bn1.weight:
   device: cuda:0
-  max: '8.985e-02'
-  mean: '-2.561e-09'
-  min: '-1.042e-01'
+  max: '9.463e-02'
+  mean: '2.008e-09'
+  min: '-8.167e-02'
   shape:
   - 256
-  sum: '-6.557e-07'
+  sum: '5.141e-07'
 grads.network.layer3.0.bn2.bias:
   device: cuda:0
-  max: '6.940e-02'
-  mean: '5.335e-04'
-  min: '-5.311e-02'
+  max: '7.878e-02'
+  mean: '-1.885e-05'
+  min: '-6.324e-02'
   shape:
   - 256
-  sum: '1.366e-01'
+  sum: '-4.826e-03'
 grads.network.layer3.0.bn2.weight:
   device: cuda:0
-  max: '5.623e-02'
-  mean: '-2.282e-08'
-  min: '-7.762e-02'
+  max: '8.373e-02'
+  mean: '1.296e-07'
+  min: '-6.153e-02'
   shape:
   - 256
-  sum: '-5.841e-06'
+  sum: '3.318e-05'
 grads.network.layer3.0.bn3.bias:
   device: cuda:0
-  max: '3.228e-02'
-  mean: '-1.181e-04'
-  min: '-2.608e-02'
+  max: '2.918e-02'
+  mean: '1.651e-04'
+  min: '-2.906e-02'
   shape:
   - 1024
-  sum: '-1.209e-01'
+  sum: '1.691e-01'
 grads.network.layer3.0.bn3.weight:
   device: cuda:0
-  max: '3.652e-02'
-  mean: '-7.228e-05'
-  min: '-4.893e-02'
+  max: '3.844e-02'
+  mean: '4.748e-04'
+  min: '-3.389e-02'
   shape:
   - 1024
-  sum: '-7.401e-02'
+  sum: '4.862e-01'
 grads.network.layer3.0.conv1.weight:
   device: cuda:0
-  max: '9.913e-02'
-  mean: '-3.902e-04'
-  min: '-9.101e-02'
+  max: '8.687e-02'
+  mean: '-7.090e-05'
+  min: '-9.015e-02'
   shape:
   - 256
   - 512
   - 1
   - 1
-  sum: '-5.114e+01'
+  sum: '-9.294e+00'
 grads.network.layer3.0.conv2.weight:
   device: cuda:0
-  max: '1.257e-01'
-  mean: '-8.546e-05'
-  min: '-1.265e-01'
+  max: '1.446e-01'
+  mean: '-2.053e-04'
+  min: '-1.556e-01'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-5.040e+01'
+  sum: '-1.211e+02'
 grads.network.layer3.0.conv3.weight:
   device: cuda:0
-  max: '9.508e-02'
-  mean: '4.733e-05'
-  min: '-1.04e-01'
+  max: '9.401e-02'
+  mean: '-1.093e-04'
+  min: '-1.06e-01'
   shape:
   - 1024
   - 256
   - 1
   - 1
-  sum: '1.241e+01'
+  sum: '-2.865e+01'
 grads.network.layer3.0.downsample.0.weight:
   device: cuda:0
-  max: '7.85e-02'
-  mean: '-3.186e-05'
-  min: '-9.409e-02'
+  max: '6.559e-02'
+  mean: '-7.776e-05'
+  min: '-7.754e-02'
   shape:
   - 1024
   - 512
   - 1
   - 1
-  sum: '-1.671e+01'
+  sum: '-4.077e+01'
 grads.network.layer3.0.downsample.1.bias:
   device: cuda:0
-  max: '3.228e-02'
-  mean: '-1.181e-04'
-  min: '-2.608e-02'
+  max: '2.918e-02'
+  mean: '1.651e-04'
+  min: '-2.906e-02'
   shape:
   - 1024
-  sum: '-1.209e-01'
+  sum: '1.691e-01'
 grads.network.layer3.0.downsample.1.weight:
   device: cuda:0
-  max: '3.657e-02'
-  mean: '-7.938e-05'
-  min: '-3.968e-02'
+  max: '3.290e-02'
+  mean: '-4.781e-04'
+  min: '-3.749e-02'
   shape:
   - 1024
-  sum: '-8.128e-02'
+  sum: '-4.896e-01'
 grads.network.layer3.1.bn1.bias:
   device: cuda:0
-  max: '5.199e-02'
-  mean: '-3.091e-04'
-  min: '-6.523e-02'
+  max: '5.626e-02'
+  mean: '4.300e-04'
+  min: '-6.352e-02'
   shape:
   - 256
-  sum: '-7.912e-02'
+  sum: '1.101e-01'
 grads.network.layer3.1.bn1.weight:
   device: cuda:0
-  max: '7.237e-02'
-  mean: '1.141e-08'
-  min: '-5.789e-02'
+  max: '6.233e-02'
+  mean: '-3.376e-09'
+  min: '-6.724e-02'
   shape:
   - 256
-  sum: '2.921e-06'
+  sum: '-8.643e-07'
 grads.network.layer3.1.bn2.bias:
   device: cuda:0
-  max: '4.225e-02'
-  mean: '7.41e-04'
-  min: '-4.171e-02'
+  max: '4.397e-02'
+  mean: '-9.819e-04'
+  min: '-4.181e-02'
   shape:
   - 256
-  sum: '1.897e-01'
+  sum: '-2.514e-01'
 grads.network.layer3.1.bn2.weight:
   device: cuda:0
-  max: '3.798e-02'
-  mean: '3.9e-08'
-  min: '-5.021e-02'
+  max: '5.085e-02'
+  mean: '-3.148e-08'
+  min: '-5.247e-02'
   shape:
   - 256
-  sum: '9.984e-06'
+  sum: '-8.058e-06'
 grads.network.layer3.1.bn3.bias:
   device: cuda:0
-  max: '1.976e-02'
-  mean: '-1.692e-04'
-  min: '-2.215e-02'
+  max: '1.751e-02'
+  mean: '-1.534e-04'
+  min: '-1.897e-02'
   shape:
   - 1024
-  sum: '-1.733e-01'
+  sum: '-1.571e-01'
 grads.network.layer3.1.bn3.weight:
   device: cuda:0
-  max: '2.348e-02'
-  mean: '1.549e-04'
-  min: '-2.379e-02'
+  max: '2.678e-02'
+  mean: '-1.272e-04'
+  min: '-2.298e-02'
   shape:
   - 1024
-  sum: '1.587e-01'
+  sum: '-1.302e-01'
 grads.network.layer3.1.conv1.weight:
   device: cuda:0
-  max: '4.929e-02'
-  mean: '4.316e-05'
-  min: '-4.696e-02'
+  max: '4.469e-02'
+  mean: '-6.691e-05'
+  min: '-5.100e-02'
   shape:
   - 256
   - 1024
   - 1
   - 1
-  sum: '1.131e+01'
+  sum: '-1.754e+01'
 grads.network.layer3.1.conv2.weight:
   device: cuda:0
-  max: '1.156e-01'
-  mean: '-8.390e-05'
-  min: '-1.048e-01'
+  max: '1.176e-01'
+  mean: '1.584e-05'
+  min: '-9.768e-02'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-4.949e+01'
+  sum: '9.344e+00'
 grads.network.layer3.1.conv3.weight:
   device: cuda:0
-  max: '6.757e-02'
-  mean: '3.39e-05'
-  min: '-6.879e-02'
+  max: '6.056e-02'
+  mean: '5.067e-05'
+  min: '-6.224e-02'
   shape:
   - 1024
   - 256
   - 1
   - 1
-  sum: '8.886e+00'
+  sum: '1.328e+01'
 grads.network.layer3.2.bn1.bias:
   device: cuda:0
-  max: '3.715e-02'
-  mean: '-3.498e-04'
-  min: '-4.113e-02'
+  max: '5.470e-02'
+  mean: '6.193e-05'
+  min: '-3.953e-02'
   shape:
   - 256
-  sum: '-8.956e-02'
+  sum: '1.585e-02'
 grads.network.layer3.2.bn1.weight:
   device: cuda:0
-  max: '4.569e-02'
-  mean: '2.867e-09'
-  min: '-4.962e-02'
+  max: '5.361e-02'
+  mean: '3.813e-09'
+  min: '-4.804e-02'
   shape:
   - 256
-  sum: '7.339e-07'
+  sum: '9.760e-07'
 grads.network.layer3.2.bn2.bias:
   device: cuda:0
-  max: '3.029e-02'
-  mean: '-4.436e-04'
-  min: '-2.692e-02'
+  max: '3.035e-02'
+  mean: '2.81e-04'
+  min: '-2.448e-02'
   shape:
   - 256
-  sum: '-1.135e-01'
+  sum: '7.193e-02'
 grads.network.layer3.2.bn2.weight:
   device: cuda:0
-  max: '3.397e-02'
-  mean: '-1.461e-08'
-  min: '-3.55e-02'
+  max: '3.848e-02'
+  mean: '-7.683e-09'
+  min: '-4.169e-02'
   shape:
   - 256
-  sum: '-3.740e-06'
+  sum: '-1.967e-06'
 grads.network.layer3.2.bn3.bias:
   device: cuda:0
-  max: '1.074e-02'
-  mean: '-9.653e-05'
-  min: '-1.428e-02'
+  max: '1.452e-02'
+  mean: '8.834e-05'
+  min: '-1.346e-02'
   shape:
   - 1024
-  sum: '-9.884e-02'
+  sum: '9.046e-02'
 grads.network.layer3.2.bn3.weight:
   device: cuda:0
-  max: '2.000e-02'
-  mean: '-7.752e-05'
-  min: '-1.676e-02'
+  max: '1.943e-02'
+  mean: '-1.422e-07'
+  min: '-2.020e-02'
   shape:
   - 1024
-  sum: '-7.938e-02'
+  sum: '-1.456e-04'
 grads.network.layer3.2.conv1.weight:
   device: cuda:0
-  max: '3.134e-02'
-  mean: '6.29e-05'
-  min: '-3.177e-02'
+  max: '3.283e-02'
+  mean: '1.224e-05'
+  min: '-2.905e-02'
   shape:
   - 256
   - 1024
   - 1
   - 1
-  sum: '1.649e+01'
+  sum: '3.209e+00'
 grads.network.layer3.2.conv2.weight:
   device: cuda:0
-  max: '7.868e-02'
-  mean: '7.155e-06'
-  min: '-7.522e-02'
+  max: '7.789e-02'
+  mean: '8.950e-05'
+  min: '-7.223e-02'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '4.220e+00'
+  sum: '5.279e+01'
 grads.network.layer3.2.conv3.weight:
   device: cuda:0
-  max: '4.457e-02'
-  mean: '-6.326e-05'
-  min: '-4.720e-02'
+  max: '4.464e-02'
+  mean: '-6.123e-05'
+  min: '-4.553e-02'
   shape:
   - 1024
   - 256
   - 1
   - 1
-  sum: '-1.658e+01'
+  sum: '-1.605e+01'
 grads.network.layer3.3.bn1.bias:
   device: cuda:0
-  max: '4.017e-02'
-  mean: '6.214e-05'
-  min: '-2.511e-02'
+  max: '3.299e-02'
+  mean: '2.663e-04'
+  min: '-2.648e-02'
   shape:
   - 256
-  sum: '1.591e-02'
+  sum: '6.817e-02'
 grads.network.layer3.3.bn1.weight:
   device: cuda:0
-  max: '3.217e-02'
-  mean: '-2.183e-10'
-  min: '-3.779e-02'
+  max: '3.304e-02'
+  mean: '2.910e-09'
+  min: '-3.094e-02'
   shape:
   - 256
-  sum: '-5.588e-08'
+  sum: '7.451e-07'
 grads.network.layer3.3.bn2.bias:
   device: cuda:0
-  max: '2.313e-02'
-  mean: '-2.275e-06'
-  min: '-2.476e-02'
+  max: '2.42e-02'
+  mean: '3.107e-04'
+  min: '-2.917e-02'
   shape:
   - 256
-  sum: '-5.825e-04'
+  sum: '7.954e-02'
 grads.network.layer3.3.bn2.weight:
   device: cuda:0
-  max: '2.436e-02'
-  mean: '-1.279e-08'
-  min: '-2.400e-02'
+  max: '2.609e-02'
+  mean: '1.887e-08'
+  min: '-2.472e-02'
   shape:
   - 256
-  sum: '-3.275e-06'
+  sum: '4.83e-06'
 grads.network.layer3.3.bn3.bias:
   device: cuda:0
-  max: '9.701e-03'
-  mean: '-4.152e-05'
-  min: '-8.985e-03'
+  max: '9.639e-03'
+  mean: '-5.944e-05'
+  min: '-8.428e-03'
   shape:
   - 1024
-  sum: '-4.251e-02'
+  sum: '-6.087e-02'
 grads.network.layer3.3.bn3.weight:
   device: cuda:0
-  max: '1.274e-02'
-  mean: '-5.492e-05'
-  min: '-1.673e-02'
+  max: '1.152e-02'
+  mean: '6.068e-05'
+  min: '-1.386e-02'
   shape:
   - 1024
-  sum: '-5.623e-02'
+  sum: '6.213e-02'
 grads.network.layer3.3.conv1.weight:
   device: cuda:0
-  max: '2.719e-02'
-  mean: '-4.864e-05'
-  min: '-2.668e-02'
+  max: '2.214e-02'
+  mean: '-1.179e-05'
+  min: '-2.13e-02'
   shape:
   - 256
   - 1024
   - 1
   - 1
-  sum: '-1.275e+01'
+  sum: '-3.09e+00'
 grads.network.layer3.3.conv2.weight:
   device: cuda:0
-  max: '6.36e-02'
-  mean: '7.046e-05'
-  min: '-5.796e-02'
+  max: '5.046e-02'
+  mean: '-4.648e-05'
+  min: '-5.283e-02'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '4.156e+01'
+  sum: '-2.742e+01'
 grads.network.layer3.3.conv3.weight:
   device: cuda:0
-  max: '4.141e-02'
-  mean: '1.489e-05'
-  min: '-3.670e-02'
+  max: '3.774e-02'
+  mean: '1.955e-05'
+  min: '-4.337e-02'
   shape:
   - 1024
   - 256
   - 1
   - 1
-  sum: '3.903e+00'
+  sum: '5.126e+00'
 grads.network.layer3.4.bn1.bias:
   device: cuda:0
-  max: '2.147e-02'
-  mean: '3.403e-05'
-  min: '-2.25e-02'
+  max: '2.209e-02'
+  mean: '5.722e-05'
+  min: '-1.97e-02'
   shape:
   - 256
-  sum: '8.711e-03'
+  sum: '1.465e-02'
 grads.network.layer3.4.bn1.weight:
   device: cuda:0
-  max: '3.626e-02'
-  mean: '-1.892e-09'
-  min: '-2.356e-02'
+  max: '3.006e-02'
+  mean: '1.688e-09'
+  min: '-2.421e-02'
   shape:
   - 256
-  sum: '-4.843e-07'
+  sum: '4.321e-07'
 grads.network.layer3.4.bn2.bias:
   device: cuda:0
-  max: '1.518e-02'
-  mean: '3.233e-04'
-  min: '-1.562e-02'
+  max: '1.791e-02'
+  mean: '6.262e-04'
+  min: '-1.803e-02'
   shape:
   - 256
-  sum: '8.277e-02'
+  sum: '1.603e-01'
 grads.network.layer3.4.bn2.weight:
   device: cuda:0
-  max: '2.106e-02'
-  mean: '4.386e-08'
-  min: '-2.206e-02'
+  max: '1.914e-02'
+  mean: '-2.16e-08'
+  min: '-2.277e-02'
   shape:
   - 256
-  sum: '1.123e-05'
+  sum: '-5.528e-06'
 grads.network.layer3.4.bn3.bias:
   device: cuda:0
-  max: '6.997e-03'
-  mean: '-6.533e-05'
-  min: '-7.944e-03'
+  max: '5.889e-03'
+  mean: '-6.465e-05'
+  min: '-6.721e-03'
   shape:
   - 1024
-  sum: '-6.689e-02'
+  sum: '-6.621e-02'
 grads.network.layer3.4.bn3.weight:
   device: cuda:0
-  max: '1.064e-02'
-  mean: '1.463e-04'
-  min: '-9.902e-03'
+  max: '1.403e-02'
+  mean: '-7.249e-05'
+  min: '-1.158e-02'
   shape:
   - 1024
-  sum: '1.498e-01'
+  sum: '-7.423e-02'
 grads.network.layer3.4.conv1.weight:
   device: cuda:0
-  max: '1.904e-02'
-  mean: '-2.754e-05'
-  min: '-1.891e-02'
+  max: '1.948e-02'
+  mean: '-5.919e-05'
+  min: '-2.123e-02'
   shape:
   - 256
   - 1024
   - 1
   - 1
-  sum: '-7.22e+00'
+  sum: '-1.552e+01'
 grads.network.layer3.4.conv2.weight:
   device: cuda:0
-  max: '4.254e-02'
-  mean: '-2.627e-05'
-  min: '-5.017e-02'
+  max: '4.519e-02'
+  mean: '-5.393e-05'
+  min: '-4.189e-02'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-1.549e+01'
+  sum: '-3.181e+01'
 grads.network.layer3.4.conv3.weight:
   device: cuda:0
-  max: '2.563e-02'
-  mean: '-3.938e-06'
-  min: '-2.833e-02'
+  max: '2.584e-02'
+  mean: '-1.662e-05'
+  min: '-3.01e-02'
   shape:
   - 1024
   - 256
   - 1
   - 1
-  sum: '-1.032e+00'
+  sum: '-4.357e+00'
 grads.network.layer3.5.bn1.bias:
   device: cuda:0
-  max: '1.901e-02'
-  mean: '2.356e-04'
-  min: '-1.961e-02'
+  max: '1.473e-02'
+  mean: '-2.863e-04'
+  min: '-2.14e-02'
   shape:
   - 256
-  sum: '6.031e-02'
+  sum: '-7.328e-02'
 grads.network.layer3.5.bn1.weight:
   device: cuda:0
-  max: '2.546e-02'
-  mean: '-9.313e-10'
-  min: '-2.608e-02'
+  max: '2.14e-02'
+  mean: '3.056e-10'
+  min: '-1.914e-02'
   shape:
   - 256
-  sum: '-2.384e-07'
+  sum: '7.823e-08'
 grads.network.layer3.5.bn2.bias:
   device: cuda:0
-  max: '1.274e-02'
-  mean: '-1.438e-04'
-  min: '-1.364e-02'
+  max: '1.543e-02'
+  mean: '4.724e-04'
+  min: '-1.144e-02'
   shape:
   - 256
-  sum: '-3.680e-02'
+  sum: '1.209e-01'
 grads.network.layer3.5.bn2.weight:
   device: cuda:0
-  max: '1.536e-02'
-  mean: '-3.012e-09'
-  min: '-2.043e-02'
+  max: '1.735e-02'
+  mean: '3.341e-08'
+  min: '-1.7e-02'
   shape:
   - 256
-  sum: '-7.711e-07'
+  sum: '8.553e-06'
 grads.network.layer3.5.bn3.bias:
   device: cuda:0
-  max: '4.202e-03'
-  mean: '-2.573e-05'
-  min: '-4.034e-03'
+  max: '4.675e-03'
+  mean: '-4.486e-05'
+  min: '-4.076e-03'
   shape:
   - 1024
-  sum: '-2.634e-02'
+  sum: '-4.593e-02'
 grads.network.layer3.5.bn3.weight:
   device: cuda:0
-  max: '9.836e-03'
-  mean: '-1.711e-05'
-  min: '-8.328e-03'
+  max: '1.022e-02'
+  mean: '1.424e-04'
+  min: '-8.853e-03'
   shape:
   - 1024
-  sum: '-1.752e-02'
+  sum: '1.459e-01'
 grads.network.layer3.5.conv1.weight:
   device: cuda:0
-  max: '1.525e-02'
-  mean: '-3.503e-05'
-  min: '-1.432e-02'
+  max: '1.520e-02'
+  mean: '-9.81e-05'
+  min: '-1.713e-02'
   shape:
   - 256
   - 1024
   - 1
   - 1
-  sum: '-9.184e+00'
+  sum: '-2.572e+01'
 grads.network.layer3.5.conv2.weight:
   device: cuda:0
-  max: '4.67e-02'
-  mean: '-7.542e-05'
-  min: '-3.959e-02'
+  max: '4.044e-02'
+  mean: '-9.633e-06'
+  min: '-3.293e-02'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-4.448e+01'
+  sum: '-5.682e+00'
 grads.network.layer3.5.conv3.weight:
   device: cuda:0
-  max: '2.486e-02'
-  mean: '-4.622e-05'
-  min: '-2.199e-02'
+  max: '2.177e-02'
+  mean: '-2.153e-05'
+  min: '-2.449e-02'
   shape:
   - 1024
   - 256
   - 1
   - 1
-  sum: '-1.212e+01'
+  sum: '-5.644e+00'
 grads.network.layer4.0.bn1.bias:
   device: cuda:0
-  max: '1.216e-02'
-  mean: '1.105e-04'
-  min: '-1.527e-02'
+  max: '1.434e-02'
+  mean: '4.065e-04'
+  min: '-1.518e-02'
   shape:
   - 512
-  sum: '5.66e-02'
+  sum: '2.081e-01'
 grads.network.layer4.0.bn1.weight:
   device: cuda:0
-  max: '1.341e-02'
-  mean: '2.454e-09'
-  min: '-1.568e-02'
+  max: '1.535e-02'
+  mean: '2.947e-09'
+  min: '-1.597e-02'
   shape:
   - 512
-  sum: '1.256e-06'
+  sum: '1.509e-06'
 grads.network.layer4.0.bn2.bias:
   device: cuda:0
-  max: '1.081e-02'
-  mean: '-9.498e-06'
-  min: '-1.008e-02'
+  max: '1.034e-02'
+  mean: '1.201e-04'
+  min: '-1.163e-02'
   shape:
   - 512
-  sum: '-4.863e-03'
+  sum: '6.148e-02'
 grads.network.layer4.0.bn2.weight:
   device: cuda:0
-  max: '1.896e-02'
-  mean: '3.362e-08'
-  min: '-1.575e-02'
+  max: '1.392e-02'
+  mean: '1.078e-08'
+  min: '-1.48e-02'
   shape:
   - 512
-  sum: '1.721e-05'
+  sum: '5.517e-06'
 grads.network.layer4.0.bn3.bias:
   device: cuda:0
-  max: '6.932e-03'
-  mean: '1.369e-04'
-  min: '-6.060e-03'
+  max: '5.379e-03'
+  mean: '7.976e-05'
+  min: '-5.568e-03'
   shape:
   - 2048
-  sum: '2.805e-01'
+  sum: '1.633e-01'
 grads.network.layer4.0.bn3.weight:
   device: cuda:0
-  max: '8.164e-03'
-  mean: '1.423e-04'
-  min: '-7.306e-03'
+  max: '7.414e-03'
+  mean: '5.28e-05'
+  min: '-6.899e-03'
   shape:
   - 2048
-  sum: '2.915e-01'
+  sum: '1.081e-01'
 grads.network.layer4.0.conv1.weight:
   device: cuda:0
-  max: '1.748e-02'
-  mean: '-2.425e-05'
-  min: '-1.699e-02'
+  max: '1.569e-02'
+  mean: '-5.496e-05'
+  min: '-1.712e-02'
   shape:
   - 512
   - 1024
   - 1
   - 1
-  sum: '-1.271e+01'
+  sum: '-2.881e+01'
 grads.network.layer4.0.conv2.weight:
   device: cuda:0
-  max: '4.355e-02'
-  mean: '-2.123e-06'
-  min: '-4.091e-02'
+  max: '4.231e-02'
+  mean: '2.069e-05'
+  min: '-4.455e-02'
   shape:
   - 512
   - 512
   - 3
   - 3
-  sum: '-5.008e+00'
+  sum: '4.881e+01'
 grads.network.layer4.0.conv3.weight:
   device: cuda:0
-  max: '1.988e-02'
-  mean: '2.471e-05'
-  min: '-2.667e-02'
+  max: '1.929e-02'
+  mean: '7.697e-06'
+  min: '-2.147e-02'
   shape:
   - 2048
   - 512
   - 1
   - 1
-  sum: '2.591e+01'
+  sum: '8.071e+00'
 grads.network.layer4.0.downsample.0.weight:
   device: cuda:0
-  max: '1.62e-02'
-  mean: '1.449e-05'
-  min: '-2.14e-02'
+  max: '1.910e-02'
+  mean: '7.601e-06'
+  min: '-1.955e-02'
   shape:
   - 2048
   - 1024
   - 1
   - 1
-  sum: '3.038e+01'
+  sum: '1.594e+01'
 grads.network.layer4.0.downsample.1.bias:
   device: cuda:0
-  max: '6.932e-03'
-  mean: '1.369e-04'
-  min: '-6.060e-03'
+  max: '5.379e-03'
+  mean: '7.976e-05'
+  min: '-5.568e-03'
   shape:
   - 2048
-  sum: '2.805e-01'
+  sum: '1.633e-01'
 grads.network.layer4.0.downsample.1.weight:
   device: cuda:0
-  max: '7.480e-03'
-  mean: '2.966e-05'
-  min: '-7.067e-03'
+  max: '7.513e-03'
+  mean: '1.056e-04'
+  min: '-1.005e-02'
   shape:
   - 2048
-  sum: '6.073e-02'
+  sum: '2.162e-01'
 grads.network.layer4.1.bn1.bias:
   device: cuda:0
-  max: '8.244e-03'
-  mean: '2.764e-05'
-  min: '-1.008e-02'
+  max: '9.774e-03'
+  mean: '-2.666e-05'
+  min: '-9.995e-03'
   shape:
   - 512
-  sum: '1.415e-02'
+  sum: '-1.365e-02'
 grads.network.layer4.1.bn1.weight:
   device: cuda:0
-  max: '1.030e-02'
-  mean: '7.094e-09'
-  min: '-1.473e-02'
+  max: '1.164e-02'
+  mean: '-2.190e-09'
+  min: '-1.019e-02'
   shape:
   - 512
-  sum: '3.632e-06'
+  sum: '-1.121e-06'
 grads.network.layer4.1.bn2.bias:
   device: cuda:0
-  max: '9.241e-03'
-  mean: '1.883e-05'
-  min: '-6.795e-03'
+  max: '8.007e-03'
+  mean: '9.899e-05'
+  min: '-8.405e-03'
   shape:
   - 512
-  sum: '9.642e-03'
+  sum: '5.068e-02'
 grads.network.layer4.1.bn2.weight:
   device: cuda:0
-  max: '9.995e-03'
-  mean: '2.548e-08'
-  min: '-9.566e-03'
+  max: '7.227e-03'
+  mean: '3.805e-08'
+  min: '-9.884e-03'
   shape:
   - 512
-  sum: '1.305e-05'
+  sum: '1.948e-05'
 grads.network.layer4.1.bn3.bias:
   device: cuda:0
-  max: '5.288e-03'
-  mean: '1.693e-04'
-  min: '-5.143e-03'
+  max: '5.638e-03'
+  mean: '1.603e-04'
+  min: '-5.243e-03'
   shape:
   - 2048
-  sum: '3.468e-01'
+  sum: '3.282e-01'
 grads.network.layer4.1.bn3.weight:
   device: cuda:0
-  max: '5.510e-03'
-  mean: '1.148e-04'
-  min: '-4.869e-03'
+  max: '6.212e-03'
+  mean: '1.651e-04'
+  min: '-5.274e-03'
   shape:
   - 2048
-  sum: '2.352e-01'
+  sum: '3.380e-01'
 grads.network.layer4.1.conv1.weight:
   device: cuda:0
-  max: '1.323e-02'
-  mean: '-7.145e-06'
-  min: '-1.063e-02'
+  max: '1.135e-02'
+  mean: '-9.175e-06'
+  min: '-1.004e-02'
   shape:
   - 512
   - 2048
   - 1
   - 1
-  sum: '-7.492e+00'
+  sum: '-9.621e+00'
 grads.network.layer4.1.conv2.weight:
   device: cuda:0
-  max: '4.482e-02'
-  mean: '4.064e-06'
-  min: '-4.435e-02'
+  max: '5.013e-02'
+  mean: '-1.012e-05'
+  min: '-5.236e-02'
   shape:
   - 512
   - 512
   - 3
   - 3
-  sum: '9.588e+00'
+  sum: '-2.387e+01'
 grads.network.layer4.1.conv3.weight:
   device: cuda:0
-  max: '1.372e-02'
-  mean: '-7.804e-07'
-  min: '-1.28e-02'
+  max: '1.501e-02'
+  mean: '8.462e-06'
+  min: '-1.297e-02'
   shape:
   - 2048
   - 512
   - 1
   - 1
-  sum: '-8.183e-01'
+  sum: '8.873e+00'
 grads.network.layer4.2.bn1.bias:
   device: cuda:0
-  max: '5.947e-03'
-  mean: '3.877e-05'
-  min: '-7.937e-03'
+  max: '6.662e-03'
+  mean: '-1.135e-05'
+  min: '-5.697e-03'
   shape:
   - 512
-  sum: '1.985e-02'
+  sum: '-5.812e-03'
 grads.network.layer4.2.bn1.weight:
   device: cuda:0
-  max: '8.022e-03'
-  mean: '1.71e-09'
-  min: '-9.428e-03'
+  max: '8.279e-03'
+  mean: '-6.748e-10'
+  min: '-7.688e-03'
   shape:
   - 512
-  sum: '8.754e-07'
+  sum: '-3.455e-07'
 grads.network.layer4.2.bn2.bias:
   device: cuda:0
-  max: '5.880e-03'
-  mean: '9.59e-05'
-  min: '-4.611e-03'
+  max: '5.914e-03'
+  mean: '-1.204e-05'
+  min: '-4.983e-03'
   shape:
   - 512
-  sum: '4.91e-02'
+  sum: '-6.166e-03'
 grads.network.layer4.2.bn2.weight:
   device: cuda:0
-  max: '7.32e-03'
-  mean: '2.751e-08'
-  min: '-5.822e-03'
+  max: '8.004e-03'
+  mean: '2.41e-08'
+  min: '-7.842e-03'
   shape:
   - 512
-  sum: '1.409e-05'
+  sum: '1.234e-05'
 grads.network.layer4.2.bn3.bias:
   device: cuda:0
-  max: '6.23e-03'
-  mean: '2.174e-04'
-  min: '-6.104e-03'
+  max: '6.687e-03'
+  mean: '2.027e-04'
+  min: '-6.187e-03'
   shape:
   - 2048
-  sum: '4.453e-01'
+  sum: '4.152e-01'
 grads.network.layer4.2.bn3.weight:
   device: cuda:0
-  max: '4.123e-03'
-  mean: '1.086e-04'
-  min: '-4.657e-03'
+  max: '4.753e-03'
+  mean: '9.091e-05'
+  min: '-4.124e-03'
   shape:
   - 2048
-  sum: '2.225e-01'
+  sum: '1.862e-01'
 grads.network.layer4.2.conv1.weight:
   device: cuda:0
-  max: '8.671e-03'
-  mean: '-1.917e-05'
-  min: '-8.358e-03'
+  max: '7.940e-03'
+  mean: '6.897e-06'
+  min: '-8.052e-03'
   shape:
   - 512
   - 2048
   - 1
   - 1
-  sum: '-2.010e+01'
+  sum: '7.232e+00'
 grads.network.layer4.2.conv2.weight:
   device: cuda:0
-  max: '3.57e-02'
-  mean: '-5.759e-06'
-  min: '-3.629e-02'
+  max: '3.132e-02'
+  mean: '5.233e-07'
+  min: '-3.756e-02'
   shape:
   - 512
   - 512
   - 3
   - 3
-  sum: '-1.359e+01'
+  sum: '1.235e+00'
 grads.network.layer4.2.conv3.weight:
   device: cuda:0
-  max: '9.38e-03'
-  mean: '2.033e-05'
-  min: '-1.081e-02'
+  max: '1.088e-02'
+  mean: '2.165e-05'
+  min: '-1.072e-02'
   shape:
   - 2048
   - 512
   - 1
   - 1
-  sum: '2.131e+01'
+  sum: '2.27e+01'
 outputs.logits:
   device: cuda:0
-  max: '5.678e+00'
-  mean: '-2.389e-03'
-  min: '-5.650e+00'
+  max: '4.328e+00'
+  mean: '-4.300e-03'
+  min: '-3.209e+00'
   shape:
   - 128
   - 10
-  sum: '-3.058e+00'
+  sum: '-5.504e+00'
 outputs.loss:
   device: cuda:0
-  max: '2.735e+00'
-  mean: '2.735e+00'
-  min: '2.735e+00'
+  max: '2.775e+00'
+  mean: '2.775e+00'
+  min: '2.775e+00'
   shape: []
-  sum: '2.735e+00'
+  sum: '2.775e+00'
 outputs.y:
   device: cuda:0
   max: 9
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet50_imagenet_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet50_imagenet_image_classifier.yaml
index 6da0613a..49049c43 100644
--- a/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet50_imagenet_image_classifier.yaml
+++ b/.regression_files/project/algorithms/image_classifier_test/test_backward_pass_is_reproducible/resnet50_imagenet_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.640e+00'
-  mean: '-6.663e-02'
+  mean: '-6.142e-02'
   min: '-2.118e+00'
   shape:
   - 64
   - 3
   - 224
   - 224
-  sum: '-6.419e+05'
+  sum: '-5.917e+05'
 batch.1:
   device: cuda:0
   max: 988
@@ -19,1468 +19,1468 @@ batch.1:
   sum: 33166
 grads.network.bn1.bias:
   device: cuda:0
-  max: '2.068e-01'
-  mean: '-9.46e-03'
-  min: '-2.002e-01'
+  max: '2.18e-01'
+  mean: '-2.921e-03'
+  min: '-2.106e-01'
   shape:
   - 64
-  sum: '-6.054e-01'
+  sum: '-1.869e-01'
 grads.network.bn1.weight:
   device: cuda:0
-  max: '2.498e-01'
-  mean: '2.254e-07'
-  min: '-3.246e-01'
+  max: '2.753e-01'
+  mean: '-7.786e-07'
+  min: '-2.226e-01'
   shape:
   - 64
-  sum: '1.442e-05'
+  sum: '-4.983e-05'
 grads.network.conv1.weight:
   device: cuda:0
-  max: '4.087e+00'
-  mean: '2.056e-01'
-  min: '-2.608e+00'
+  max: '4.245e+00'
+  mean: '6.171e-02'
+  min: '-3.546e+00'
   shape:
   - 64
   - 3
   - 7
   - 7
-  sum: '1.934e+03'
+  sum: '5.806e+02'
 grads.network.fc.bias:
   device: cuda:0
-  max: '4.933e-03'
-  mean: '-2.235e-11'
+  max: '4.852e-03'
+  mean: '-2.980e-11'
   min: '-3.081e-02'
   shape:
   - 1000
-  sum: '-2.235e-08'
+  sum: '-2.980e-08'
 grads.network.fc.weight:
   device: cuda:0
-  max: '9.717e-03'
+  max: '9.609e-03'
   mean: '-1.118e-11'
-  min: '-9.624e-02'
+  min: '-1.637e-01'
   shape:
   - 1000
   - 2048
   sum: '-2.289e-05'
 grads.network.layer1.0.bn1.bias:
   device: cuda:0
-  max: '1.701e-01'
-  mean: '-1.097e-02'
-  min: '-2.24e-01'
+  max: '1.581e-01'
+  mean: '8.436e-03'
+  min: '-1.496e-01'
   shape:
   - 64
-  sum: '-7.022e-01'
+  sum: '5.399e-01'
 grads.network.layer1.0.bn1.weight:
   device: cuda:0
-  max: '2.153e-01'
-  mean: '-6.054e-09'
-  min: '-2.101e-01'
+  max: '3.167e-01'
+  mean: '1.034e-07'
+  min: '-1.860e-01'
   shape:
   - 64
-  sum: '-3.874e-07'
+  sum: '6.616e-06'
 grads.network.layer1.0.bn2.bias:
   device: cuda:0
-  max: '2.238e-01'
-  mean: '2.082e-03'
-  min: '-1.410e-01'
+  max: '1.395e-01'
+  mean: '8.096e-03'
+  min: '-1.714e-01'
   shape:
   - 64
-  sum: '1.333e-01'
+  sum: '5.182e-01'
 grads.network.layer1.0.bn2.weight:
   device: cuda:0
-  max: '1.821e-01'
-  mean: '-9.057e-08'
-  min: '-2.169e-01'
+  max: '1.84e-01'
+  mean: '6.992e-07'
+  min: '-1.664e-01'
   shape:
   - 64
-  sum: '-5.797e-06'
+  sum: '4.475e-05'
 grads.network.layer1.0.bn3.bias:
   device: cuda:0
-  max: '6.3e-02'
-  mean: '-6.664e-04'
-  min: '-6.507e-02'
+  max: '7.000e-02'
+  mean: '5.642e-04'
+  min: '-7.241e-02'
   shape:
   - 256
-  sum: '-1.706e-01'
+  sum: '1.444e-01'
 grads.network.layer1.0.bn3.weight:
   device: cuda:0
-  max: '9.049e-02'
-  mean: '-6.014e-04'
-  min: '-9.014e-02'
+  max: '1.100e-01'
+  mean: '2.122e-03'
+  min: '-1.005e-01'
   shape:
   - 256
-  sum: '-1.539e-01'
+  sum: '5.433e-01'
 grads.network.layer1.0.conv1.weight:
   device: cuda:0
-  max: '3.310e-01'
-  mean: '-6.233e-04'
-  min: '-4.917e-01'
+  max: '5.983e-01'
+  mean: '-2.526e-03'
+  min: '-4.016e-01'
   shape:
   - 64
   - 64
   - 1
   - 1
-  sum: '-2.553e+00'
+  sum: '-1.035e+01'
 grads.network.layer1.0.conv2.weight:
   device: cuda:0
-  max: '2.914e-01'
-  mean: '1.291e-03'
-  min: '-3.517e-01'
+  max: '3.269e-01'
+  mean: '-3.498e-04'
+  min: '-3.289e-01'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '4.760e+01'
+  sum: '-1.289e+01'
 grads.network.layer1.0.conv3.weight:
   device: cuda:0
-  max: '2.922e-01'
-  mean: '9.76e-04'
-  min: '-2.715e-01'
+  max: '2.628e-01'
+  mean: '1.411e-04'
+  min: '-2.826e-01'
   shape:
   - 256
   - 64
   - 1
   - 1
-  sum: '1.599e+01'
+  sum: '2.312e+00'
 grads.network.layer1.0.downsample.0.weight:
   device: cuda:0
-  max: '3.240e-01'
-  mean: '6.147e-04'
-  min: '-4.201e-01'
+  max: '3.524e-01'
+  mean: '8.336e-04'
+  min: '-4.161e-01'
   shape:
   - 256
   - 64
   - 1
   - 1
-  sum: '1.007e+01'
+  sum: '1.366e+01'
 grads.network.layer1.0.downsample.1.bias:
   device: cuda:0
-  max: '6.3e-02'
-  mean: '-6.664e-04'
-  min: '-6.507e-02'
+  max: '7.000e-02'
+  mean: '5.642e-04'
+  min: '-7.241e-02'
   shape:
   - 256
-  sum: '-1.706e-01'
+  sum: '1.444e-01'
 grads.network.layer1.0.downsample.1.weight:
   device: cuda:0
-  max: '1.168e-01'
-  mean: '8.313e-04'
-  min: '-7.264e-02'
+  max: '1.067e-01'
+  mean: '-1.766e-03'
+  min: '-8.789e-02'
   shape:
   - 256
-  sum: '2.128e-01'
+  sum: '-4.521e-01'
 grads.network.layer1.1.bn1.bias:
   device: cuda:0
-  max: '1.160e-01'
-  mean: '9.456e-04'
-  min: '-1.079e-01'
+  max: '1.222e-01'
+  mean: '-4.960e-03'
+  min: '-1.378e-01'
   shape:
   - 64
-  sum: '6.052e-02'
+  sum: '-3.174e-01'
 grads.network.layer1.1.bn1.weight:
   device: cuda:0
-  max: '1.274e-01'
-  mean: '3.097e-08'
-  min: '-1.296e-01'
+  max: '1.819e-01'
+  mean: '1.7e-08'
+  min: '-1.339e-01'
   shape:
   - 64
-  sum: '1.982e-06'
+  sum: '1.088e-06'
 grads.network.layer1.1.bn2.bias:
   device: cuda:0
-  max: '9.845e-02'
-  mean: '5.403e-03'
-  min: '-7.661e-02'
+  max: '1.051e-01'
+  mean: '7.113e-03'
+  min: '-8.361e-02'
   shape:
   - 64
-  sum: '3.458e-01'
+  sum: '4.552e-01'
 grads.network.layer1.1.bn2.weight:
   device: cuda:0
-  max: '1.274e-01'
-  mean: '-4.994e-08'
-  min: '-1.105e-01'
+  max: '1.175e-01'
+  mean: '-1.674e-07'
+  min: '-1.093e-01'
   shape:
   - 64
-  sum: '-3.196e-06'
+  sum: '-1.071e-05'
 grads.network.layer1.1.bn3.bias:
   device: cuda:0
-  max: '4.778e-02'
-  mean: '9.509e-04'
-  min: '-3.793e-02'
+  max: '3.679e-02'
+  mean: '-1.322e-03'
+  min: '-4.954e-02'
   shape:
   - 256
-  sum: '2.434e-01'
+  sum: '-3.386e-01'
 grads.network.layer1.1.bn3.weight:
   device: cuda:0
-  max: '7.710e-02'
-  mean: '2.718e-04'
-  min: '-5.506e-02'
+  max: '5.422e-02'
+  mean: '-1.085e-03'
+  min: '-5.978e-02'
   shape:
   - 256
-  sum: '6.959e-02'
+  sum: '-2.779e-01'
 grads.network.layer1.1.conv1.weight:
   device: cuda:0
-  max: '1.421e-01'
-  mean: '3.867e-04'
-  min: '-1.254e-01'
+  max: '1.202e-01'
+  mean: '7.560e-04'
+  min: '-1.251e-01'
   shape:
   - 64
   - 256
   - 1
   - 1
-  sum: '6.335e+00'
+  sum: '1.239e+01'
 grads.network.layer1.1.conv2.weight:
   device: cuda:0
-  max: '2.049e-01'
-  mean: '-3.724e-04'
-  min: '-2.049e-01'
+  max: '2.116e-01'
+  mean: '5.928e-04'
+  min: '-1.983e-01'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '-1.373e+01'
+  sum: '2.185e+01'
 grads.network.layer1.1.conv3.weight:
   device: cuda:0
-  max: '1.850e-01'
-  mean: '-1.549e-04'
-  min: '-1.803e-01'
+  max: '1.527e-01'
+  mean: '8.327e-05'
+  min: '-1.538e-01'
   shape:
   - 256
   - 64
   - 1
   - 1
-  sum: '-2.539e+00'
+  sum: '1.364e+00'
 grads.network.layer1.2.bn1.bias:
   device: cuda:0
-  max: '5.462e-02'
-  mean: '-5.246e-04'
-  min: '-8.094e-02'
+  max: '9.774e-02'
+  mean: '-1.289e-03'
+  min: '-9.675e-02'
   shape:
   - 64
-  sum: '-3.358e-02'
+  sum: '-8.25e-02'
 grads.network.layer1.2.bn1.weight:
   device: cuda:0
-  max: '1.337e-01'
-  mean: '9.662e-09'
-  min: '-7.616e-02'
+  max: '1.051e-01'
+  mean: '2.026e-08'
+  min: '-9.671e-02'
   shape:
   - 64
-  sum: '6.184e-07'
+  sum: '1.296e-06'
 grads.network.layer1.2.bn2.bias:
   device: cuda:0
-  max: '5.837e-02'
-  mean: '-2.464e-04'
-  min: '-6.975e-02'
+  max: '3.952e-02'
+  mean: '-7.389e-04'
+  min: '-7.078e-02'
   shape:
   - 64
-  sum: '-1.577e-02'
+  sum: '-4.729e-02'
 grads.network.layer1.2.bn2.weight:
   device: cuda:0
-  max: '7.667e-02'
-  mean: '-1.267e-07'
-  min: '-6.187e-02'
+  max: '6.634e-02'
+  mean: '2.142e-07'
+  min: '-8.625e-02'
   shape:
   - 64
-  sum: '-8.106e-06'
+  sum: '1.371e-05'
 grads.network.layer1.2.bn3.bias:
   device: cuda:0
-  max: '2.286e-02'
-  mean: '7.026e-04'
-  min: '-2.327e-02'
+  max: '2.835e-02'
+  mean: '4.330e-04'
+  min: '-2.508e-02'
   shape:
   - 256
-  sum: '1.799e-01'
+  sum: '1.108e-01'
 grads.network.layer1.2.bn3.weight:
   device: cuda:0
-  max: '4.287e-02'
-  mean: '-5.017e-04'
-  min: '-4.000e-02'
+  max: '6.014e-02'
+  mean: '7.293e-04'
+  min: '-4.68e-02'
   shape:
   - 256
-  sum: '-1.284e-01'
+  sum: '1.867e-01'
 grads.network.layer1.2.conv1.weight:
   device: cuda:0
-  max: '8.545e-02'
-  mean: '-3.494e-04'
-  min: '-9.286e-02'
+  max: '8.867e-02'
+  mean: '-3.021e-04'
+  min: '-7.584e-02'
   shape:
   - 64
   - 256
   - 1
   - 1
-  sum: '-5.725e+00'
+  sum: '-4.949e+00'
 grads.network.layer1.2.conv2.weight:
   device: cuda:0
-  max: '1.467e-01'
-  mean: '-1.392e-04'
-  min: '-1.282e-01'
+  max: '1.531e-01'
+  mean: '9.553e-04'
+  min: '-1.571e-01'
   shape:
   - 64
   - 64
   - 3
   - 3
-  sum: '-5.132e+00'
+  sum: '3.522e+01'
 grads.network.layer1.2.conv3.weight:
   device: cuda:0
-  max: '1.048e-01'
-  mean: '-1.928e-04'
-  min: '-1.267e-01'
+  max: '1.007e-01'
+  mean: '3.110e-04'
+  min: '-1.036e-01'
   shape:
   - 256
   - 64
   - 1
   - 1
-  sum: '-3.16e+00'
+  sum: '5.096e+00'
 grads.network.layer2.0.bn1.bias:
   device: cuda:0
-  max: '4.211e-02'
-  mean: '1.735e-03'
-  min: '-5.167e-02'
+  max: '6.093e-02'
+  mean: '1.488e-03'
+  min: '-5.451e-02'
   shape:
   - 128
-  sum: '2.221e-01'
+  sum: '1.904e-01'
 grads.network.layer2.0.bn1.weight:
   device: cuda:0
-  max: '4.957e-02'
-  mean: '8.149e-09'
-  min: '-4.993e-02'
+  max: '5.444e-02'
+  mean: '-1.572e-09'
+  min: '-7.364e-02'
   shape:
   - 128
-  sum: '1.043e-06'
+  sum: '-2.012e-07'
 grads.network.layer2.0.bn2.bias:
   device: cuda:0
-  max: '3.316e-02'
-  mean: '7.625e-04'
-  min: '-3.657e-02'
+  max: '3.219e-02'
+  mean: '5.006e-04'
+  min: '-4.727e-02'
   shape:
   - 128
-  sum: '9.760e-02'
+  sum: '6.408e-02'
 grads.network.layer2.0.bn2.weight:
   device: cuda:0
-  max: '5.121e-02'
-  mean: '-4.243e-08'
-  min: '-4.316e-02'
+  max: '4.038e-02'
+  mean: '6.828e-08'
+  min: '-4.888e-02'
   shape:
   - 128
-  sum: '-5.431e-06'
+  sum: '8.74e-06'
 grads.network.layer2.0.bn3.bias:
   device: cuda:0
-  max: '2.226e-02'
-  mean: '1.177e-04'
-  min: '-1.811e-02'
+  max: '1.987e-02'
+  mean: '3.367e-05'
+  min: '-2.030e-02'
   shape:
   - 512
-  sum: '6.026e-02'
+  sum: '1.724e-02'
 grads.network.layer2.0.bn3.weight:
   device: cuda:0
-  max: '2.429e-02'
-  mean: '-2.402e-04'
-  min: '-2.550e-02'
+  max: '2.435e-02'
+  mean: '1.763e-04'
+  min: '-2.518e-02'
   shape:
   - 512
-  sum: '-1.230e-01'
+  sum: '9.024e-02'
 grads.network.layer2.0.conv1.weight:
   device: cuda:0
-  max: '8.179e-02'
-  mean: '-1.704e-05'
-  min: '-7.493e-02'
+  max: '7.369e-02'
+  mean: '-1.615e-04'
+  min: '-6.874e-02'
   shape:
   - 128
   - 256
   - 1
   - 1
-  sum: '-5.582e-01'
+  sum: '-5.291e+00'
 grads.network.layer2.0.conv2.weight:
   device: cuda:0
-  max: '8.488e-02'
-  mean: '-2.583e-04'
-  min: '-8.498e-02'
+  max: '7.794e-02'
+  mean: '-2.011e-04'
+  min: '-8.008e-02'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '-3.809e+01'
+  sum: '-2.965e+01'
 grads.network.layer2.0.conv3.weight:
   device: cuda:0
-  max: '7.02e-02'
-  mean: '1.67e-05'
-  min: '-7.408e-02'
+  max: '6.737e-02'
+  mean: '-1.725e-04'
+  min: '-7.077e-02'
   shape:
   - 512
   - 128
   - 1
   - 1
-  sum: '1.094e+00'
+  sum: '-1.131e+01'
 grads.network.layer2.0.downsample.0.weight:
   device: cuda:0
-  max: '5.65e-02'
-  mean: '3.045e-05'
-  min: '-5.636e-02'
+  max: '5.762e-02'
+  mean: '-9.190e-05'
+  min: '-4.896e-02'
   shape:
   - 512
   - 256
   - 1
   - 1
-  sum: '3.991e+00'
+  sum: '-1.205e+01'
 grads.network.layer2.0.downsample.1.bias:
   device: cuda:0
-  max: '2.226e-02'
-  mean: '1.177e-04'
-  min: '-1.811e-02'
+  max: '1.987e-02'
+  mean: '3.367e-05'
+  min: '-2.030e-02'
   shape:
   - 512
-  sum: '6.026e-02'
+  sum: '1.724e-02'
 grads.network.layer2.0.downsample.1.weight:
   device: cuda:0
-  max: '2.814e-02'
-  mean: '4.625e-04'
-  min: '-2.305e-02'
+  max: '2.493e-02'
+  mean: '-1.618e-04'
+  min: '-2.705e-02'
   shape:
   - 512
-  sum: '2.368e-01'
+  sum: '-8.284e-02'
 grads.network.layer2.1.bn1.bias:
   device: cuda:0
-  max: '3.645e-02'
-  mean: '-7.118e-04'
-  min: '-3.115e-02'
+  max: '3.816e-02'
+  mean: '6.147e-04'
+  min: '-2.575e-02'
   shape:
   - 128
-  sum: '-9.111e-02'
+  sum: '7.868e-02'
 grads.network.layer2.1.bn1.weight:
   device: cuda:0
-  max: '4.458e-02'
-  mean: '-6.869e-09'
-  min: '-3.865e-02'
+  max: '3.029e-02'
+  mean: '-7.974e-09'
+  min: '-3.427e-02'
   shape:
   - 128
-  sum: '-8.792e-07'
+  sum: '-1.021e-06'
 grads.network.layer2.1.bn2.bias:
   device: cuda:0
-  max: '2.695e-02'
-  mean: '-9.38e-04'
-  min: '-2.543e-02'
+  max: '2.880e-02'
+  mean: '2.14e-04'
+  min: '-2.289e-02'
   shape:
   - 128
-  sum: '-1.201e-01'
+  sum: '2.739e-02'
 grads.network.layer2.1.bn2.weight:
   device: cuda:0
-  max: '2.824e-02'
-  mean: '-1.768e-08'
-  min: '-2.943e-02'
+  max: '2.687e-02'
+  mean: '-2.331e-08'
+  min: '-2.677e-02'
   shape:
   - 128
-  sum: '-2.263e-06'
+  sum: '-2.984e-06'
 grads.network.layer2.1.bn3.bias:
   device: cuda:0
-  max: '1.148e-02'
-  mean: '2.42e-04'
-  min: '-9.819e-03'
+  max: '1.077e-02'
+  mean: '1.248e-04'
+  min: '-1.136e-02'
   shape:
   - 512
-  sum: '1.239e-01'
+  sum: '6.388e-02'
 grads.network.layer2.1.bn3.weight:
   device: cuda:0
   max: '1.542e-02'
-  mean: '-9.633e-05'
-  min: '-1.593e-02'
+  mean: '-1.305e-04'
+  min: '-1.882e-02'
   shape:
   - 512
-  sum: '-4.932e-02'
+  sum: '-6.68e-02'
 grads.network.layer2.1.conv1.weight:
   device: cuda:0
-  max: '3.077e-02'
-  mean: '3.157e-04'
-  min: '-3.122e-02'
+  max: '3.084e-02'
+  mean: '-1.191e-04'
+  min: '-3.066e-02'
   shape:
   - 128
   - 512
   - 1
   - 1
-  sum: '2.069e+01'
+  sum: '-7.805e+00'
 grads.network.layer2.1.conv2.weight:
   device: cuda:0
-  max: '5.878e-02'
-  mean: '5.832e-05'
-  min: '-5.409e-02'
+  max: '5.597e-02'
+  mean: '3.056e-05'
+  min: '-5.399e-02'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '8.600e+00'
+  sum: '4.506e+00'
 grads.network.layer2.1.conv3.weight:
   device: cuda:0
-  max: '5.426e-02'
-  mean: '6.567e-05'
-  min: '-3.881e-02'
+  max: '5.019e-02'
+  mean: '2.466e-05'
+  min: '-4.123e-02'
   shape:
   - 512
   - 128
   - 1
   - 1
-  sum: '4.303e+00'
+  sum: '1.616e+00'
 grads.network.layer2.2.bn1.bias:
   device: cuda:0
-  max: '3.436e-02'
-  mean: '1.063e-05'
-  min: '-2.625e-02'
+  max: '2.609e-02'
+  mean: '-7.58e-04'
+  min: '-2.585e-02'
   shape:
   - 128
-  sum: '1.361e-03'
+  sum: '-9.702e-02'
 grads.network.layer2.2.bn1.weight:
   device: cuda:0
-  max: '2.442e-02'
-  mean: '-6.228e-09'
-  min: '-3.548e-02'
+  max: '2.496e-02'
+  mean: '2.037e-09'
+  min: '-3.202e-02'
   shape:
   - 128
-  sum: '-7.972e-07'
+  sum: '2.608e-07'
 grads.network.layer2.2.bn2.bias:
   device: cuda:0
-  max: '1.91e-02'
-  mean: '8.820e-05'
-  min: '-1.719e-02'
+  max: '1.844e-02'
+  mean: '-7.005e-05'
+  min: '-1.728e-02'
   shape:
   - 128
-  sum: '1.129e-02'
+  sum: '-8.967e-03'
 grads.network.layer2.2.bn2.weight:
   device: cuda:0
-  max: '2.045e-02'
-  mean: '7.683e-09'
-  min: '-2.136e-02'
+  max: '3.135e-02'
+  mean: '-2.072e-08'
+  min: '-1.652e-02'
   shape:
   - 128
-  sum: '9.835e-07'
+  sum: '-2.652e-06'
 grads.network.layer2.2.bn3.bias:
   device: cuda:0
-  max: '7.928e-03'
-  mean: '-9.574e-05'
-  min: '-7.345e-03'
+  max: '8.718e-03'
+  mean: '-3.033e-05'
+  min: '-8.8e-03'
   shape:
   - 512
-  sum: '-4.902e-02'
+  sum: '-1.553e-02'
 grads.network.layer2.2.bn3.weight:
   device: cuda:0
-  max: '1.170e-02'
-  mean: '2.873e-05'
-  min: '-1.136e-02'
+  max: '1.077e-02'
+  mean: '-1.305e-04'
+  min: '-1.098e-02'
   shape:
   - 512
-  sum: '1.471e-02'
+  sum: '-6.682e-02'
 grads.network.layer2.2.conv1.weight:
   device: cuda:0
-  max: '2.182e-02'
-  mean: '5.088e-05'
-  min: '-2.084e-02'
+  max: '2.180e-02'
+  mean: '6.494e-07'
+  min: '-2.462e-02'
   shape:
   - 128
   - 512
   - 1
   - 1
-  sum: '3.334e+00'
+  sum: '4.256e-02'
 grads.network.layer2.2.conv2.weight:
   device: cuda:0
-  max: '4.288e-02'
-  mean: '-5.458e-05'
-  min: '-4.216e-02'
+  max: '3.634e-02'
+  mean: '-2.338e-05'
+  min: '-3.72e-02'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '-8.048e+00'
+  sum: '-3.447e+00'
 grads.network.layer2.2.conv3.weight:
   device: cuda:0
-  max: '3.284e-02'
-  mean: '4.204e-05'
-  min: '-3.245e-02'
+  max: '2.904e-02'
+  mean: '-4.951e-05'
+  min: '-3.298e-02'
   shape:
   - 512
   - 128
   - 1
   - 1
-  sum: '2.755e+00'
+  sum: '-3.245e+00'
 grads.network.layer2.3.bn1.bias:
   device: cuda:0
-  max: '1.834e-02'
-  mean: '4.186e-04'
-  min: '-2.066e-02'
+  max: '2.347e-02'
+  mean: '5.434e-04'
+  min: '-1.930e-02'
   shape:
   - 128
-  sum: '5.358e-02'
+  sum: '6.956e-02'
 grads.network.layer2.3.bn1.weight:
   device: cuda:0
-  max: '2.448e-02'
-  mean: '-2.095e-09'
-  min: '-2.123e-02'
+  max: '1.864e-02'
+  mean: '-3.463e-09'
+  min: '-1.725e-02'
   shape:
   - 128
-  sum: '-2.682e-07'
+  sum: '-4.433e-07'
 grads.network.layer2.3.bn2.bias:
   device: cuda:0
-  max: '1.283e-02'
-  mean: '2.229e-04'
-  min: '-1.321e-02'
+  max: '1.485e-02'
+  mean: '4.036e-04'
+  min: '-1.565e-02'
   shape:
   - 128
-  sum: '2.853e-02'
+  sum: '5.166e-02'
 grads.network.layer2.3.bn2.weight:
   device: cuda:0
-  max: '1.610e-02'
-  mean: '-3.396e-08'
-  min: '-2.095e-02'
+  max: '1.985e-02'
+  mean: '5.224e-08'
+  min: '-1.859e-02'
   shape:
   - 128
-  sum: '-4.347e-06'
+  sum: '6.687e-06'
 grads.network.layer2.3.bn3.bias:
   device: cuda:0
-  max: '4.654e-03'
-  mean: '-2.983e-05'
-  min: '-5.059e-03'
+  max: '5.853e-03'
+  mean: '6.317e-05'
+  min: '-6.522e-03'
   shape:
   - 512
-  sum: '-1.527e-02'
+  sum: '3.234e-02'
 grads.network.layer2.3.bn3.weight:
   device: cuda:0
-  max: '1.013e-02'
-  mean: '-1.547e-04'
-  min: '-1.059e-02'
+  max: '7.753e-03'
+  mean: '2.465e-04'
+  min: '-8.944e-03'
   shape:
   - 512
-  sum: '-7.918e-02'
+  sum: '1.262e-01'
 grads.network.layer2.3.conv1.weight:
   device: cuda:0
-  max: '1.884e-02'
-  mean: '1.101e-04'
-  min: '-1.608e-02'
+  max: '1.605e-02'
+  mean: '-1.146e-04'
+  min: '-1.844e-02'
   shape:
   - 128
   - 512
   - 1
   - 1
-  sum: '7.213e+00'
+  sum: '-7.513e+00'
 grads.network.layer2.3.conv2.weight:
   device: cuda:0
-  max: '2.661e-02'
-  mean: '6.131e-05'
-  min: '-2.643e-02'
+  max: '3.384e-02'
+  mean: '-1.192e-04'
+  min: '-3.263e-02'
   shape:
   - 128
   - 128
   - 3
   - 3
-  sum: '9.040e+00'
+  sum: '-1.758e+01'
 grads.network.layer2.3.conv3.weight:
   device: cuda:0
-  max: '2.310e-02'
-  mean: '4.181e-05'
-  min: '-2.429e-02'
+  max: '2.375e-02'
+  mean: '-8.01e-07'
+  min: '-2.232e-02'
   shape:
   - 512
   - 128
   - 1
   - 1
-  sum: '2.74e+00'
+  sum: '-5.249e-02'
 grads.network.layer3.0.bn1.bias:
   device: cuda:0
-  max: '1.159e-02'
-  mean: '6.957e-05'
-  min: '-1.154e-02'
+  max: '1.146e-02'
+  mean: '-1.418e-04'
+  min: '-1.122e-02'
   shape:
   - 256
-  sum: '1.781e-02'
+  sum: '-3.63e-02'
 grads.network.layer3.0.bn1.weight:
   device: cuda:0
-  max: '1.38e-02'
-  mean: '-4.657e-10'
-  min: '-1.321e-02'
+  max: '1.433e-02'
+  mean: '-8.440e-10'
+  min: '-1.535e-02'
   shape:
   - 256
-  sum: '-1.192e-07'
+  sum: '-2.161e-07'
 grads.network.layer3.0.bn2.bias:
   device: cuda:0
-  max: '1.036e-02'
-  mean: '1.608e-04'
-  min: '-1.092e-02'
+  max: '9.935e-03'
+  mean: '-9.778e-05'
+  min: '-9.152e-03'
   shape:
   - 256
-  sum: '4.116e-02'
+  sum: '-2.503e-02'
 grads.network.layer3.0.bn2.weight:
   device: cuda:0
-  max: '1.286e-02'
-  mean: '-9.262e-09'
-  min: '-1.329e-02'
+  max: '1.179e-02'
+  mean: '5.537e-09'
+  min: '-1.047e-02'
   shape:
   - 256
-  sum: '-2.371e-06'
+  sum: '1.417e-06'
 grads.network.layer3.0.bn3.bias:
   device: cuda:0
-  max: '4.818e-03'
-  mean: '1.895e-05'
-  min: '-4.491e-03'
+  max: '4.930e-03'
+  mean: '-1.128e-08'
+  min: '-5.811e-03'
   shape:
   - 1024
-  sum: '1.940e-02'
+  sum: '-1.155e-05'
 grads.network.layer3.0.bn3.weight:
   device: cuda:0
-  max: '6.393e-03'
-  mean: '-5.269e-05'
-  min: '-5.746e-03'
+  max: '5.871e-03'
+  mean: '4.149e-05'
+  min: '-7.131e-03'
   shape:
   - 1024
-  sum: '-5.396e-02'
+  sum: '4.249e-02'
 grads.network.layer3.0.conv1.weight:
   device: cuda:0
-  max: '1.654e-02'
-  mean: '-4.966e-05'
-  min: '-1.824e-02'
+  max: '1.444e-02'
+  mean: '-6.213e-05'
+  min: '-1.865e-02'
   shape:
   - 256
   - 512
   - 1
   - 1
-  sum: '-6.51e+00'
+  sum: '-8.143e+00'
 grads.network.layer3.0.conv2.weight:
   device: cuda:0
-  max: '1.841e-02'
-  mean: '-1.719e-05'
-  min: '-1.882e-02'
+  max: '1.892e-02'
+  mean: '-4.419e-06'
+  min: '-1.984e-02'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-1.014e+01'
+  sum: '-2.606e+00'
 grads.network.layer3.0.conv3.weight:
   device: cuda:0
-  max: '1.641e-02'
-  mean: '-2.978e-05'
-  min: '-1.824e-02'
+  max: '1.562e-02'
+  mean: '7.211e-06'
+  min: '-1.537e-02'
   shape:
   - 1024
   - 256
   - 1
   - 1
-  sum: '-7.806e+00'
+  sum: '1.890e+00'
 grads.network.layer3.0.downsample.0.weight:
   device: cuda:0
-  max: '1.271e-02'
-  mean: '-2.944e-05'
-  min: '-1.281e-02'
+  max: '1.236e-02'
+  mean: '1.92e-05'
+  min: '-1.257e-02'
   shape:
   - 1024
   - 512
   - 1
   - 1
-  sum: '-1.544e+01'
+  sum: '1.007e+01'
 grads.network.layer3.0.downsample.1.bias:
   device: cuda:0
-  max: '4.818e-03'
-  mean: '1.895e-05'
-  min: '-4.491e-03'
+  max: '4.930e-03'
+  mean: '-1.128e-08'
+  min: '-5.811e-03'
   shape:
   - 1024
-  sum: '1.940e-02'
+  sum: '-1.155e-05'
 grads.network.layer3.0.downsample.1.weight:
   device: cuda:0
-  max: '7.039e-03'
-  mean: '-1.403e-05'
-  min: '-5.472e-03'
+  max: '6.960e-03'
+  mean: '-3.118e-05'
+  min: '-7.090e-03'
   shape:
   - 1024
-  sum: '-1.437e-02'
+  sum: '-3.193e-02'
 grads.network.layer3.1.bn1.bias:
   device: cuda:0
-  max: '1.027e-02'
-  mean: '-7.899e-05'
-  min: '-7.042e-03'
+  max: '7.982e-03'
+  mean: '9.037e-05'
+  min: '-8.511e-03'
   shape:
   - 256
-  sum: '-2.022e-02'
+  sum: '2.313e-02'
 grads.network.layer3.1.bn1.weight:
   device: cuda:0
-  max: '9.592e-03'
-  mean: '-1.186e-09'
-  min: '-9.877e-03'
+  max: '9.757e-03'
+  mean: '1.521e-09'
+  min: '-1.001e-02'
   shape:
   - 256
-  sum: '-3.036e-07'
+  sum: '3.893e-07'
 grads.network.layer3.1.bn2.bias:
   device: cuda:0
-  max: '5.802e-03'
-  mean: '-1.144e-04'
-  min: '-6.516e-03'
+  max: '6.475e-03'
+  mean: '4.268e-05'
+  min: '-5.562e-03'
   shape:
   - 256
-  sum: '-2.929e-02'
+  sum: '1.093e-02'
 grads.network.layer3.1.bn2.weight:
   device: cuda:0
-  max: '7.174e-03'
-  mean: '1.312e-08'
-  min: '-7.594e-03'
+  max: '7.610e-03'
+  mean: '2.656e-09'
+  min: '-7.943e-03'
   shape:
   - 256
-  sum: '3.359e-06'
+  sum: '6.799e-07'
 grads.network.layer3.1.bn3.bias:
   device: cuda:0
-  max: '2.986e-03'
-  mean: '-8.18e-06'
-  min: '-3.319e-03'
+  max: '3.427e-03'
+  mean: '2.818e-05'
+  min: '-3.057e-03'
   shape:
   - 1024
-  sum: '-8.376e-03'
+  sum: '2.885e-02'
 grads.network.layer3.1.bn3.weight:
   device: cuda:0
-  max: '4.028e-03'
-  mean: '6.062e-05'
-  min: '-3.991e-03'
+  max: '4.061e-03'
+  mean: '7.217e-06'
+  min: '-4.201e-03'
   shape:
   - 1024
-  sum: '6.207e-02'
+  sum: '7.39e-03'
 grads.network.layer3.1.conv1.weight:
   device: cuda:0
-  max: '8.729e-03'
-  mean: '-2.166e-05'
-  min: '-7.953e-03'
+  max: '8.042e-03'
+  mean: '9.029e-06'
+  min: '-8.126e-03'
   shape:
   - 256
   - 1024
   - 1
   - 1
-  sum: '-5.678e+00'
+  sum: '2.367e+00'
 grads.network.layer3.1.conv2.weight:
   device: cuda:0
-  max: '1.39e-02'
-  mean: '-2.612e-05'
-  min: '-1.387e-02'
+  max: '1.384e-02'
+  mean: '-1.74e-05'
+  min: '-1.336e-02'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-1.541e+01'
+  sum: '-1.026e+01'
 grads.network.layer3.1.conv3.weight:
   device: cuda:0
-  max: '1.024e-02'
-  mean: '-1.092e-05'
-  min: '-1.074e-02'
+  max: '1.066e-02'
+  mean: '-1.192e-05'
+  min: '-1.009e-02'
   shape:
   - 1024
   - 256
   - 1
   - 1
-  sum: '-2.863e+00'
+  sum: '-3.126e+00'
 grads.network.layer3.2.bn1.bias:
   device: cuda:0
-  max: '7.474e-03'
-  mean: '1.205e-04'
-  min: '-6.481e-03'
+  max: '4.814e-03'
+  mean: '-2.040e-05'
+  min: '-7.328e-03'
   shape:
   - 256
-  sum: '3.085e-02'
+  sum: '-5.223e-03'
 grads.network.layer3.2.bn1.weight:
   device: cuda:0
-  max: '9.865e-03'
-  mean: '-9.313e-10'
-  min: '-7.930e-03'
+  max: '9.034e-03'
+  mean: '-5.748e-10'
+  min: '-6.375e-03'
   shape:
   - 256
-  sum: '-2.384e-07'
+  sum: '-1.471e-07'
 grads.network.layer3.2.bn2.bias:
   device: cuda:0
-  max: '5.072e-03'
-  mean: '1.298e-04'
-  min: '-4.838e-03'
+  max: '4.063e-03'
+  mean: '-7.406e-05'
+  min: '-5.289e-03'
   shape:
   - 256
-  sum: '3.323e-02'
+  sum: '-1.896e-02'
 grads.network.layer3.2.bn2.weight:
   device: cuda:0
-  max: '6.424e-03'
-  mean: '9.468e-09'
-  min: '-5.991e-03'
+  max: '6.779e-03'
+  mean: '1.979e-09'
+  min: '-5.132e-03'
   shape:
   - 256
-  sum: '2.424e-06'
+  sum: '5.066e-07'
 grads.network.layer3.2.bn3.bias:
   device: cuda:0
-  max: '1.696e-03'
-  mean: '2.526e-05'
-  min: '-1.766e-03'
+  max: '2.172e-03'
+  mean: '2.152e-06'
+  min: '-1.718e-03'
   shape:
   - 1024
-  sum: '2.587e-02'
+  sum: '2.204e-03'
 grads.network.layer3.2.bn3.weight:
   device: cuda:0
-  max: '3.010e-03'
-  mean: '3.859e-05'
-  min: '-2.832e-03'
+  max: '3.146e-03'
+  mean: '4.660e-06'
+  min: '-3.676e-03'
   shape:
   - 1024
-  sum: '3.952e-02'
+  sum: '4.772e-03'
 grads.network.layer3.2.conv1.weight:
   device: cuda:0
-  max: '6.116e-03'
-  mean: '-1.069e-05'
-  min: '-6.560e-03'
+  max: '5.969e-03'
+  mean: '-9.190e-06'
+  min: '-8.629e-03'
   shape:
   - 256
   - 1024
   - 1
   - 1
-  sum: '-2.802e+00'
+  sum: '-2.409e+00'
 grads.network.layer3.2.conv2.weight:
   device: cuda:0
-  max: '9.867e-03'
-  mean: '-6.347e-06'
-  min: '-9.511e-03'
+  max: '9.128e-03'
+  mean: '-2.499e-05'
+  min: '-9.966e-03'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-3.744e+00'
+  sum: '-1.474e+01'
 grads.network.layer3.2.conv3.weight:
   device: cuda:0
-  max: '7.406e-03'
-  mean: '-2.159e-05'
-  min: '-7.51e-03'
+  max: '8.039e-03'
+  mean: '-2.710e-06'
+  min: '-7.601e-03'
   shape:
   - 1024
   - 256
   - 1
   - 1
-  sum: '-5.66e+00'
+  sum: '-7.105e-01'
 grads.network.layer3.3.bn1.bias:
   device: cuda:0
-  max: '3.839e-03'
-  mean: '4.194e-05'
-  min: '-4.033e-03'
+  max: '3.625e-03'
+  mean: '6.761e-05'
+  min: '-4.452e-03'
   shape:
   - 256
-  sum: '1.074e-02'
+  sum: '1.731e-02'
 grads.network.layer3.3.bn1.weight:
   device: cuda:0
-  max: '5.956e-03'
-  mean: '1.382e-10'
-  min: '-5.073e-03'
+  max: '5.844e-03'
+  mean: '-8.004e-11'
+  min: '-7.490e-03'
   shape:
   - 256
-  sum: '3.539e-08'
+  sum: '-2.049e-08'
 grads.network.layer3.3.bn2.bias:
   device: cuda:0
-  max: '4.210e-03'
-  mean: '3.714e-05'
-  min: '-3.497e-03'
+  max: '3.061e-03'
+  mean: '2.556e-05'
+  min: '-3.242e-03'
   shape:
   - 256
-  sum: '9.507e-03'
+  sum: '6.542e-03'
 grads.network.layer3.3.bn2.weight:
   device: cuda:0
-  max: '4.847e-03'
-  mean: '-6.614e-09'
-  min: '-4.154e-03'
+  max: '4.446e-03'
+  mean: '-2.139e-09'
+  min: '-5.4e-03'
   shape:
   - 256
-  sum: '-1.693e-06'
+  sum: '-5.476e-07'
 grads.network.layer3.3.bn3.bias:
   device: cuda:0
-  max: '1.448e-03'
-  mean: '1.18e-05'
-  min: '-1.585e-03'
+  max: '1.436e-03'
+  mean: '2.737e-06'
+  min: '-1.275e-03'
   shape:
   - 1024
-  sum: '1.208e-02'
+  sum: '2.803e-03'
 grads.network.layer3.3.bn3.weight:
   device: cuda:0
-  max: '2.472e-03'
-  mean: '-3.084e-05'
-  min: '-2.461e-03'
+  max: '2.207e-03'
+  mean: '-6.253e-06'
+  min: '-2.149e-03'
   shape:
   - 1024
-  sum: '-3.158e-02'
+  sum: '-6.403e-03'
 grads.network.layer3.3.conv1.weight:
   device: cuda:0
-  max: '4.561e-03'
-  mean: '-1.505e-06'
-  min: '-4.213e-03'
+  max: '4.816e-03'
+  mean: '-2.427e-05'
+  min: '-4.666e-03'
   shape:
   - 256
   - 1024
   - 1
   - 1
-  sum: '-3.946e-01'
+  sum: '-6.362e+00'
 grads.network.layer3.3.conv2.weight:
   device: cuda:0
-  max: '7.155e-03'
-  mean: '-1.727e-05'
-  min: '-7.462e-03'
+  max: '7.769e-03'
+  mean: '-3.081e-05'
+  min: '-7.682e-03'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-1.019e+01'
+  sum: '-1.817e+01'
 grads.network.layer3.3.conv3.weight:
   device: cuda:0
-  max: '7.199e-03'
-  mean: '-1.848e-05'
-  min: '-6.481e-03'
+  max: '6.32e-03'
+  mean: '-1.382e-05'
+  min: '-5.843e-03'
   shape:
   - 1024
   - 256
   - 1
   - 1
-  sum: '-4.844e+00'
+  sum: '-3.623e+00'
 grads.network.layer3.4.bn1.bias:
   device: cuda:0
-  max: '3.403e-03'
-  mean: '2.286e-05'
-  min: '-3.422e-03'
+  max: '3.067e-03'
+  mean: '1.794e-05'
+  min: '-3.405e-03'
   shape:
   - 256
-  sum: '5.853e-03'
+  sum: '4.592e-03'
 grads.network.layer3.4.bn1.weight:
   device: cuda:0
-  max: '3.392e-03'
-  mean: '7.512e-10'
-  min: '-4.168e-03'
+  max: '4.485e-03'
+  mean: '-1.652e-09'
+  min: '-4.173e-03'
   shape:
   - 256
-  sum: '1.923e-07'
+  sum: '-4.228e-07'
 grads.network.layer3.4.bn2.bias:
   device: cuda:0
-  max: '2.511e-03'
-  mean: '5.277e-05'
-  min: '-3.381e-03'
+  max: '2.896e-03'
+  mean: '2.245e-05'
+  min: '-2.966e-03'
   shape:
   - 256
-  sum: '1.351e-02'
+  sum: '5.747e-03'
 grads.network.layer3.4.bn2.weight:
   device: cuda:0
-  max: '4.038e-03'
-  mean: '3.572e-09'
-  min: '-3.609e-03'
+  max: '3.466e-03'
+  mean: '-5.618e-09'
+  min: '-3.857e-03'
   shape:
   - 256
-  sum: '9.146e-07'
+  sum: '-1.438e-06'
 grads.network.layer3.4.bn3.bias:
   device: cuda:0
-  max: '1.408e-03'
-  mean: '1.227e-05'
-  min: '-8.456e-04'
+  max: '8.637e-04'
+  mean: '7.039e-06'
+  min: '-9.596e-04'
   shape:
   - 1024
-  sum: '1.256e-02'
+  sum: '7.208e-03'
 grads.network.layer3.4.bn3.weight:
   device: cuda:0
-  max: '1.611e-03'
-  mean: '1.336e-05'
-  min: '-1.889e-03'
+  max: '1.935e-03'
+  mean: '-2.568e-05'
+  min: '-2.001e-03'
   shape:
   - 1024
-  sum: '1.368e-02'
+  sum: '-2.63e-02'
 grads.network.layer3.4.conv1.weight:
   device: cuda:0
-  max: '3.532e-03'
-  mean: '-8.469e-06'
-  min: '-4.099e-03'
+  max: '3.442e-03'
+  mean: '-1.324e-06'
+  min: '-3.592e-03'
   shape:
   - 256
   - 1024
   - 1
   - 1
-  sum: '-2.220e+00'
+  sum: '-3.470e-01'
 grads.network.layer3.4.conv2.weight:
   device: cuda:0
-  max: '5.658e-03'
-  mean: '-1.714e-05'
-  min: '-5.384e-03'
+  max: '5.916e-03'
+  mean: '-5.083e-06'
+  min: '-5.278e-03'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-1.011e+01'
+  sum: '-2.998e+00'
 grads.network.layer3.4.conv3.weight:
   device: cuda:0
-  max: '4.909e-03'
-  mean: '-1.151e-05'
-  min: '-4.874e-03'
+  max: '4.755e-03'
+  mean: '-1.294e-05'
+  min: '-4.574e-03'
   shape:
   - 1024
   - 256
   - 1
   - 1
-  sum: '-3.016e+00'
+  sum: '-3.391e+00'
 grads.network.layer3.5.bn1.bias:
   device: cuda:0
-  max: '2.425e-03'
-  mean: '-1.526e-05'
-  min: '-2.448e-03'
+  max: '2.876e-03'
+  mean: '7.039e-05'
+  min: '-2.512e-03'
   shape:
   - 256
-  sum: '-3.906e-03'
+  sum: '1.802e-02'
 grads.network.layer3.5.bn1.weight:
   device: cuda:0
-  max: '3.617e-03'
-  mean: '7.203e-10'
-  min: '-2.678e-03'
+  max: '3.697e-03'
+  mean: '-4.002e-11'
+  min: '-3.132e-03'
   shape:
   - 256
-  sum: '1.844e-07'
+  sum: '-1.024e-08'
 grads.network.layer3.5.bn2.bias:
   device: cuda:0
-  max: '2.354e-03'
-  mean: '5.188e-05'
-  min: '-3.471e-03'
+  max: '2.142e-03'
+  mean: '3.737e-05'
+  min: '-2.895e-03'
   shape:
   - 256
-  sum: '1.328e-02'
+  sum: '9.566e-03'
 grads.network.layer3.5.bn2.weight:
   device: cuda:0
-  max: '2.992e-03'
-  mean: '-3.147e-09'
-  min: '-2.420e-03'
+  max: '2.912e-03'
+  mean: '1.481e-09'
+  min: '-3.191e-03'
   shape:
   - 256
-  sum: '-8.056e-07'
+  sum: '3.790e-07'
 grads.network.layer3.5.bn3.bias:
   device: cuda:0
-  max: '6.43e-04'
-  mean: '8.147e-06'
-  min: '-6.512e-04'
+  max: '6.093e-04'
+  mean: '1.961e-06'
+  min: '-6.732e-04'
   shape:
   - 1024
-  sum: '8.342e-03'
+  sum: '2.008e-03'
 grads.network.layer3.5.bn3.weight:
   device: cuda:0
-  max: '1.439e-03'
-  mean: '-1.501e-05'
-  min: '-1.433e-03'
+  max: '1.548e-03'
+  mean: '9.746e-06'
+  min: '-1.482e-03'
   shape:
   - 1024
-  sum: '-1.537e-02'
+  sum: '9.980e-03'
 grads.network.layer3.5.conv1.weight:
   device: cuda:0
-  max: '2.588e-03'
-  mean: '-1.225e-05'
-  min: '-3.101e-03'
+  max: '2.845e-03'
+  mean: '-3.633e-06'
+  min: '-3.464e-03'
   shape:
   - 256
   - 1024
   - 1
   - 1
-  sum: '-3.211e+00'
+  sum: '-9.523e-01'
 grads.network.layer3.5.conv2.weight:
   device: cuda:0
-  max: '4.908e-03'
-  mean: '-1.443e-05'
-  min: '-4.324e-03'
+  max: '4.662e-03'
+  mean: '-2.532e-05'
+  min: '-4.75e-03'
   shape:
   - 256
   - 256
   - 3
   - 3
-  sum: '-8.509e+00'
+  sum: '-1.493e+01'
 grads.network.layer3.5.conv3.weight:
   device: cuda:0
-  max: '4.695e-03'
-  mean: '-1.048e-05'
-  min: '-4.000e-03'
+  max: '3.467e-03'
+  mean: '-1.518e-05'
+  min: '-4.239e-03'
   shape:
   - 1024
   - 256
   - 1
   - 1
-  sum: '-2.746e+00'
+  sum: '-3.98e+00'
 grads.network.layer4.0.bn1.bias:
   device: cuda:0
-  max: '2.172e-03'
-  mean: '-1.531e-06'
-  min: '-2.475e-03'
+  max: '2.133e-03'
+  mean: '6.255e-05'
+  min: '-1.732e-03'
   shape:
   - 512
-  sum: '-7.838e-04'
+  sum: '3.203e-02'
 grads.network.layer4.0.bn1.weight:
   device: cuda:0
-  max: '2.885e-03'
-  mean: '1.164e-10'
-  min: '-3.367e-03'
+  max: '2.756e-03'
+  mean: '1.537e-10'
+  min: '-2.559e-03'
   shape:
   - 512
-  sum: '5.960e-08'
+  sum: '7.87e-08'
 grads.network.layer4.0.bn2.bias:
   device: cuda:0
-  max: '1.743e-03'
-  mean: '4.506e-05'
-  min: '-1.865e-03'
+  max: '1.966e-03'
+  mean: '3.604e-06'
+  min: '-1.974e-03'
   shape:
   - 512
-  sum: '2.307e-02'
+  sum: '1.845e-03'
 grads.network.layer4.0.bn2.weight:
   device: cuda:0
-  max: '2.32e-03'
-  mean: '1.145e-08'
-  min: '-3.617e-03'
+  max: '3.044e-03'
+  mean: '8.595e-09'
+  min: '-3.107e-03'
   shape:
   - 512
-  sum: '5.864e-06'
+  sum: '4.400e-06'
 grads.network.layer4.0.bn3.bias:
   device: cuda:0
-  max: '2.545e-03'
-  mean: '8.033e-05'
-  min: '-2.183e-03'
+  max: '2.446e-03'
+  mean: '6.891e-05'
+  min: '-2.189e-03'
   shape:
   - 2048
-  sum: '1.645e-01'
+  sum: '1.411e-01'
 grads.network.layer4.0.bn3.weight:
   device: cuda:0
-  max: '2.965e-03'
-  mean: '4.471e-05'
-  min: '-2.004e-03'
+  max: '2.912e-03'
+  mean: '3.539e-05'
+  min: '-2.097e-03'
   shape:
   - 2048
-  sum: '9.156e-02'
+  sum: '7.248e-02'
 grads.network.layer4.0.conv1.weight:
   device: cuda:0
-  max: '3.048e-03'
-  mean: '-1.777e-05'
-  min: '-2.91e-03'
+  max: '3.491e-03'
+  mean: '-1.472e-05'
+  min: '-3.866e-03'
   shape:
   - 512
   - 1024
   - 1
   - 1
-  sum: '-9.317e+00'
+  sum: '-7.717e+00'
 grads.network.layer4.0.conv2.weight:
   device: cuda:0
-  max: '4.142e-03'
-  mean: '-8.243e-06'
-  min: '-3.973e-03'
+  max: '4.313e-03'
+  mean: '-4.551e-06'
+  min: '-4.408e-03'
   shape:
   - 512
   - 512
   - 3
   - 3
-  sum: '-1.945e+01'
+  sum: '-1.074e+01'
 grads.network.layer4.0.conv3.weight:
   device: cuda:0
-  max: '3.856e-03'
-  mean: '-4.106e-06'
-  min: '-4.645e-03'
+  max: '4.868e-03'
+  mean: '-6.167e-06'
+  min: '-4.588e-03'
   shape:
   - 2048
   - 512
   - 1
   - 1
-  sum: '-4.306e+00'
+  sum: '-6.466e+00'
 grads.network.layer4.0.downsample.0.weight:
   device: cuda:0
-  max: '3.427e-03'
-  mean: '1.003e-06'
-  min: '-3.696e-03'
+  max: '3.984e-03'
+  mean: '-2.024e-06'
+  min: '-3.743e-03'
   shape:
   - 2048
   - 1024
   - 1
   - 1
-  sum: '2.104e+00'
+  sum: '-4.244e+00'
 grads.network.layer4.0.downsample.1.bias:
   device: cuda:0
-  max: '2.545e-03'
-  mean: '8.033e-05'
-  min: '-2.183e-03'
+  max: '2.446e-03'
+  mean: '6.891e-05'
+  min: '-2.189e-03'
   shape:
   - 2048
-  sum: '1.645e-01'
+  sum: '1.411e-01'
 grads.network.layer4.0.downsample.1.weight:
   device: cuda:0
-  max: '2.177e-03'
-  mean: '3.785e-05'
-  min: '-2.256e-03'
+  max: '2.667e-03'
+  mean: '5.218e-05'
+  min: '-2.020e-03'
   shape:
   - 2048
-  sum: '7.751e-02'
+  sum: '1.069e-01'
 grads.network.layer4.1.bn1.bias:
   device: cuda:0
-  max: '1.501e-03'
-  mean: '2.144e-05'
-  min: '-1.368e-03'
+  max: '1.617e-03'
+  mean: '1.156e-05'
+  min: '-1.530e-03'
   shape:
   - 512
-  sum: '1.098e-02'
+  sum: '5.917e-03'
 grads.network.layer4.1.bn1.weight:
   device: cuda:0
-  max: '2.379e-03'
-  mean: '7.913e-11'
-  min: '-2.5e-03'
+  max: '2.683e-03'
+  mean: '-2.074e-10'
+  min: '-2.723e-03'
   shape:
   - 512
-  sum: '4.051e-08'
+  sum: '-1.062e-07'
 grads.network.layer4.1.bn2.bias:
   device: cuda:0
-  max: '1.778e-03'
-  mean: '4.209e-05'
-  min: '-1.812e-03'
+  max: '1.503e-03'
+  mean: '3.279e-05'
+  min: '-1.393e-03'
   shape:
   - 512
-  sum: '2.155e-02'
+  sum: '1.679e-02'
 grads.network.layer4.1.bn2.weight:
   device: cuda:0
-  max: '2.058e-03'
-  mean: '1.25e-08'
-  min: '-2.322e-03'
+  max: '2.422e-03'
+  mean: '1.119e-08'
+  min: '-3.537e-03'
   shape:
   - 512
-  sum: '6.399e-06'
+  sum: '5.727e-06'
 grads.network.layer4.1.bn3.bias:
   device: cuda:0
-  max: '2.914e-03'
-  mean: '1.136e-04'
-  min: '-3.222e-03'
+  max: '3.133e-03'
+  mean: '1.058e-04'
+  min: '-3.272e-03'
   shape:
   - 2048
-  sum: '2.327e-01'
+  sum: '2.167e-01'
 grads.network.layer4.1.bn3.weight:
   device: cuda:0
-  max: '2.364e-03'
-  mean: '5.421e-05'
-  min: '-2.150e-03'
+  max: '2.335e-03'
+  mean: '4.958e-05'
+  min: '-2.246e-03'
   shape:
   - 2048
-  sum: '1.110e-01'
+  sum: '1.015e-01'
 grads.network.layer4.1.conv1.weight:
   device: cuda:0
-  max: '1.885e-03'
-  mean: '-2.997e-06'
-  min: '-1.927e-03'
+  max: '2.076e-03'
+  mean: '-3.061e-07'
+  min: '-2.112e-03'
   shape:
   - 512
   - 2048
   - 1
   - 1
-  sum: '-3.143e+00'
+  sum: '-3.209e-01'
 grads.network.layer4.1.conv2.weight:
   device: cuda:0
-  max: '3.744e-03'
-  mean: '-1.002e-05'
-  min: '-3.811e-03'
+  max: '3.265e-03'
+  mean: '-7.268e-06'
+  min: '-4.186e-03'
   shape:
   - 512
   - 512
   - 3
   - 3
-  sum: '-2.364e+01'
+  sum: '-1.715e+01'
 grads.network.layer4.1.conv3.weight:
   device: cuda:0
-  max: '5.011e-03'
-  mean: '2.916e-07'
-  min: '-3.704e-03'
+  max: '4.766e-03'
+  mean: '-8.553e-07'
+  min: '-4.377e-03'
   shape:
   - 2048
   - 512
   - 1
   - 1
-  sum: '3.058e-01'
+  sum: '-8.968e-01'
 grads.network.layer4.2.bn1.bias:
   device: cuda:0
-  max: '1.331e-03'
-  mean: '2.21e-05'
-  min: '-1.425e-03'
+  max: '1.928e-03'
+  mean: '2.11e-05'
+  min: '-1.462e-03'
   shape:
   - 512
-  sum: '1.131e-02'
+  sum: '1.080e-02'
 grads.network.layer4.2.bn1.weight:
   device: cuda:0
-  max: '2.19e-03'
-  mean: '2.183e-10'
-  min: '-2.435e-03'
+  max: '2.295e-03'
+  mean: '8.913e-11'
+  min: '-2.387e-03'
   shape:
   - 512
-  sum: '1.118e-07'
+  sum: '4.563e-08'
 grads.network.layer4.2.bn2.bias:
   device: cuda:0
-  max: '1.404e-03'
-  mean: '9.475e-06'
-  min: '-1.412e-03'
+  max: '1.383e-03'
+  mean: '-1.383e-05'
+  min: '-1.916e-03'
   shape:
   - 512
-  sum: '4.851e-03'
+  sum: '-7.079e-03'
 grads.network.layer4.2.bn2.weight:
   device: cuda:0
-  max: '3.054e-03'
-  mean: '1.17e-08'
-  min: '-2.907e-03'
+  max: '3.125e-03'
+  mean: '1.362e-08'
+  min: '-3.191e-03'
   shape:
   - 512
-  sum: '5.990e-06'
+  sum: '6.972e-06'
 grads.network.layer4.2.bn3.bias:
   device: cuda:0
-  max: '4.169e-03'
-  mean: '1.393e-04'
-  min: '-4.317e-03'
+  max: '4.240e-03'
+  mean: '1.411e-04'
+  min: '-4.313e-03'
   shape:
   - 2048
-  sum: '2.852e-01'
+  sum: '2.890e-01'
 grads.network.layer4.2.bn3.weight:
   device: cuda:0
-  max: '2.599e-03'
-  mean: '5.148e-05'
-  min: '-1.775e-03'
+  max: '2.122e-03'
+  mean: '5.847e-05'
+  min: '-2.053e-03'
   shape:
   - 2048
-  sum: '1.054e-01'
+  sum: '1.198e-01'
 grads.network.layer4.2.conv1.weight:
   device: cuda:0
-  max: '1.832e-03'
-  mean: '-4.348e-06'
-  min: '-1.785e-03'
+  max: '1.872e-03'
+  mean: '-1.806e-06'
+  min: '-1.805e-03'
   shape:
   - 512
   - 2048
   - 1
   - 1
-  sum: '-4.559e+00'
+  sum: '-1.893e+00'
 grads.network.layer4.2.conv2.weight:
   device: cuda:0
-  max: '4.026e-03'
-  mean: '4.673e-06'
-  min: '-3.410e-03'
+  max: '4.681e-03'
+  mean: '2.802e-06'
+  min: '-3.280e-03'
   shape:
   - 512
   - 512
   - 3
   - 3
-  sum: '1.102e+01'
+  sum: '6.611e+00'
 grads.network.layer4.2.conv3.weight:
   device: cuda:0
-  max: '4.736e-03'
-  mean: '-5.085e-06'
-  min: '-4.618e-03'
+  max: '4.932e-03'
+  mean: '-2.475e-06'
+  min: '-4.53e-03'
   shape:
   - 2048
   - 512
   - 1
   - 1
-  sum: '-5.332e+00'
+  sum: '-2.595e+00'
 outputs.logits:
   device: cuda:0
-  max: '4.058e+00'
-  mean: '1.188e-02'
-  min: '-4.237e+00'
+  max: '4.872e+00'
+  mean: '1.169e-02'
+  min: '-5.017e+00'
   shape:
   - 64
   - 1000
-  sum: '7.600e+02'
+  sum: '7.483e+02'
 outputs.loss:
   device: cuda:0
-  max: '7.112e+00'
-  mean: '7.112e+00'
-  min: '7.112e+00'
+  max: '7.132e+00'
+  mean: '7.132e+00'
+  min: '7.132e+00'
   shape: []
-  sum: '7.112e+00'
+  sum: '7.132e+00'
 outputs.y:
   device: cuda:0
   max: 988
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/fcnet_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/fcnet_cifar10_image_classifier.yaml
new file mode 100644
index 00000000..511ef9e8
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/fcnet_cifar10_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 3
+  - 32
+  - 32
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '8.260e-02'
+  mean: '-5.284e-03'
+  min: '-8.901e-02'
+  shape:
+  - 128
+  - 10
+  sum: '-6.764e+00'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/fcnet_fashion_mnist_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/fcnet_fashion_mnist_image_classifier.yaml
new file mode 100644
index 00000000..10843c9e
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/fcnet_fashion_mnist_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 1
+  - 28
+  - 28
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '5.177e-02'
+  mean: '-3.37e-02'
+  min: '-8.578e-02'
+  shape:
+  - 128
+  - 10
+  sum: '-4.313e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/fcnet_mnist_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/fcnet_mnist_image_classifier.yaml
new file mode 100644
index 00000000..10843c9e
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/fcnet_mnist_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 1
+  - 28
+  - 28
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '5.177e-02'
+  mean: '-3.37e-02'
+  min: '-8.578e-02'
+  shape:
+  - 128
+  - 10
+  sum: '-4.313e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/resnet18_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/resnet18_cifar10_image_classifier.yaml
new file mode 100644
index 00000000..daa8da37
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/resnet18_cifar10_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 3
+  - 32
+  - 32
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '4.314e-02'
+  mean: '2.057e-04'
+  min: '-3.14e-02'
+  shape:
+  - 128
+  - 10
+  sum: '2.633e-01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/resnet18_imagenet_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/resnet18_imagenet_image_classifier.yaml
new file mode 100644
index 00000000..c4e885b1
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/resnet18_imagenet_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 64
+  - 3
+  - 224
+  - 224
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '4.419e-02'
+  mean: '1.212e-06'
+  min: '-4.419e-02'
+  shape:
+  - 64
+  - 1000
+  sum: '7.757e-02'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/resnet50_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/resnet50_cifar10_image_classifier.yaml
new file mode 100644
index 00000000..21ac7ac7
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/resnet50_cifar10_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 3
+  - 32
+  - 32
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '2.199e-02'
+  mean: '3.231e-03'
+  min: '-2.176e-02'
+  shape:
+  - 128
+  - 10
+  sum: '4.136e+00'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/resnet50_imagenet_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/resnet50_imagenet_image_classifier.yaml
new file mode 100644
index 00000000..f28279f6
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cpu/resnet50_imagenet_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 64
+  - 3
+  - 224
+  - 224
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '2.203e-02'
+  mean: '4.486e-04'
+  min: '-2.206e-02'
+  shape:
+  - 64
+  - 1000
+  sum: '2.871e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/fcnet_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/fcnet_cifar10_image_classifier.yaml
deleted file mode 100644
index dad2fb47..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/fcnet_cifar10_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.126e+00'
-  mean: '-6.179e-03'
-  min: '-1.989e+00'
-  shape:
-  - 128
-  - 3
-  - 32
-  - 32
-  sum: '-2.43e+03'
-out:
-  device: cuda:0
-  max: '7.036e-01'
-  mean: '-8.651e-03'
-  min: '-8.180e-01'
-  shape:
-  - 128
-  - 10
-  sum: '-1.107e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/fcnet_fashion_mnist_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/fcnet_fashion_mnist_image_classifier.yaml
deleted file mode 100644
index 005a43b1..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/fcnet_fashion_mnist_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.821e+00'
-  mean: '4.822e-01'
-  min: '-4.242e-01'
-  shape:
-  - 128
-  - 1
-  - 28
-  - 28
-  sum: '4.839e+04'
-out:
-  device: cuda:0
-  max: '9.872e-01'
-  mean: '-1.288e-02'
-  min: '-7.225e-01'
-  shape:
-  - 128
-  - 10
-  sum: '-1.648e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/fcnet_mnist_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/fcnet_mnist_image_classifier.yaml
deleted file mode 100644
index 459b4d35..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/fcnet_mnist_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.821e+00'
-  mean: '1.432e-02'
-  min: '-4.242e-01'
-  shape:
-  - 128
-  - 1
-  - 28
-  - 28
-  sum: '1.437e+03'
-out:
-  device: cuda:0
-  max: '7.029e-01'
-  mean: '-3.564e-02'
-  min: '-7.781e-01'
-  shape:
-  - 128
-  - 10
-  sum: '-4.562e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/resnet18_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/resnet18_cifar10_image_classifier.yaml
deleted file mode 100644
index 82be89f1..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/resnet18_cifar10_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.126e+00'
-  mean: '-6.179e-03'
-  min: '-1.989e+00'
-  shape:
-  - 128
-  - 3
-  - 32
-  - 32
-  sum: '-2.43e+03'
-out:
-  device: cuda:0
-  max: '2.728e+00'
-  mean: '8.106e-02'
-  min: '-2.536e+00'
-  shape:
-  - 128
-  - 10
-  sum: '1.038e+02'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/resnet18_imagenet_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/resnet18_imagenet_image_classifier.yaml
deleted file mode 100644
index 071379c4..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/resnet18_imagenet_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.640e+00'
-  mean: '-6.663e-02'
-  min: '-2.118e+00'
-  shape:
-  - 64
-  - 3
-  - 224
-  - 224
-  sum: '-6.419e+05'
-out:
-  device: cuda:0
-  max: '2.934e+00'
-  mean: '-8.071e-04'
-  min: '-2.896e+00'
-  shape:
-  - 64
-  - 1000
-  sum: '-5.165e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/resnet50_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/resnet50_cifar10_image_classifier.yaml
deleted file mode 100644
index d0f19aa4..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/resnet50_cifar10_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.126e+00'
-  mean: '-6.179e-03'
-  min: '-1.989e+00'
-  shape:
-  - 128
-  - 3
-  - 32
-  - 32
-  sum: '-2.43e+03'
-out:
-  device: cuda:0
-  max: '5.678e+00'
-  mean: '-2.389e-03'
-  min: '-5.650e+00'
-  shape:
-  - 128
-  - 10
-  sum: '-3.058e+00'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/resnet50_imagenet_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/resnet50_imagenet_image_classifier.yaml
deleted file mode 100644
index bfd8d4f6..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_forward_pass_is_reproducible/cuda/resnet50_imagenet_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.640e+00'
-  mean: '-6.663e-02'
-  min: '-2.118e+00'
-  shape:
-  - 64
-  - 3
-  - 224
-  - 224
-  sum: '-6.419e+05'
-out:
-  device: cuda:0
-  max: '4.058e+00'
-  mean: '1.188e-02'
-  min: '-4.237e+00'
-  shape:
-  - 64
-  - 1000
-  sum: '7.600e+02'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/fcnet_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/fcnet_cifar10_image_classifier.yaml
deleted file mode 100644
index 1018428b..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/fcnet_cifar10_image_classifier.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-network.0.1.bias:
-  device: cuda:0
-  max: '1.801e-02'
-  mean: '1.029e-03'
-  min: '-1.784e-02'
-  shape:
-  - 128
-  sum: '1.317e-01'
-network.0.1.weight:
-  device: cuda:0
-  max: '1.804e-02'
-  mean: '1.616e-05'
-  min: '-1.804e-02'
-  shape:
-  - 128
-  - 3072
-  sum: '6.354e+00'
-network.1.0.bias:
-  device: cuda:0
-  max: '8.781e-02'
-  mean: '4.829e-04'
-  min: '-8.787e-02'
-  shape:
-  - 128
-  sum: '6.181e-02'
-network.1.0.weight:
-  device: cuda:0
-  max: '8.837e-02'
-  mean: '-9.613e-04'
-  min: '-8.837e-02'
-  shape:
-  - 128
-  - 128
-  sum: '-1.575e+01'
-network.2.0.bias:
-  device: cuda:0
-  max: '8.495e-02'
-  mean: '-9.068e-04'
-  min: '-8.834e-02'
-  shape:
-  - 10
-  sum: '-9.068e-03'
-network.2.0.weight:
-  device: cuda:0
-  max: '8.826e-02'
-  mean: '-3.724e-04'
-  min: '-8.834e-02'
-  shape:
-  - 10
-  - 128
-  sum: '-4.767e-01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/fcnet_fashion_mnist_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/fcnet_fashion_mnist_image_classifier.yaml
deleted file mode 100644
index c85a5f80..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/fcnet_fashion_mnist_image_classifier.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-network.0.1.bias:
-  device: cuda:0
-  max: '3.530e-02'
-  mean: '1.341e-03'
-  min: '-3.541e-02'
-  shape:
-  - 128
-  sum: '1.716e-01'
-network.0.1.weight:
-  device: cuda:0
-  max: '3.571e-02'
-  mean: '9.349e-05'
-  min: '-3.571e-02'
-  shape:
-  - 128
-  - 784
-  sum: '9.382e+00'
-network.1.0.bias:
-  device: cuda:0
-  max: '8.268e-02'
-  mean: '-6.752e-03'
-  min: '-8.591e-02'
-  shape:
-  - 128
-  sum: '-8.642e-01'
-network.1.0.weight:
-  device: cuda:0
-  max: '8.837e-02'
-  mean: '1.286e-04'
-  min: '-8.838e-02'
-  shape:
-  - 128
-  - 128
-  sum: '2.107e+00'
-network.2.0.bias:
-  device: cuda:0
-  max: '4.038e-02'
-  mean: '-3.545e-02'
-  min: '-7.938e-02'
-  shape:
-  - 10
-  sum: '-3.545e-01'
-network.2.0.weight:
-  device: cuda:0
-  max: '8.829e-02'
-  mean: '-5.307e-04'
-  min: '-8.835e-02'
-  shape:
-  - 10
-  - 128
-  sum: '-6.793e-01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/fcnet_mnist_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/fcnet_mnist_image_classifier.yaml
deleted file mode 100644
index c85a5f80..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/fcnet_mnist_image_classifier.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-network.0.1.bias:
-  device: cuda:0
-  max: '3.530e-02'
-  mean: '1.341e-03'
-  min: '-3.541e-02'
-  shape:
-  - 128
-  sum: '1.716e-01'
-network.0.1.weight:
-  device: cuda:0
-  max: '3.571e-02'
-  mean: '9.349e-05'
-  min: '-3.571e-02'
-  shape:
-  - 128
-  - 784
-  sum: '9.382e+00'
-network.1.0.bias:
-  device: cuda:0
-  max: '8.268e-02'
-  mean: '-6.752e-03'
-  min: '-8.591e-02'
-  shape:
-  - 128
-  sum: '-8.642e-01'
-network.1.0.weight:
-  device: cuda:0
-  max: '8.837e-02'
-  mean: '1.286e-04'
-  min: '-8.838e-02'
-  shape:
-  - 128
-  - 128
-  sum: '2.107e+00'
-network.2.0.bias:
-  device: cuda:0
-  max: '4.038e-02'
-  mean: '-3.545e-02'
-  min: '-7.938e-02'
-  shape:
-  - 10
-  sum: '-3.545e-01'
-network.2.0.weight:
-  device: cuda:0
-  max: '8.829e-02'
-  mean: '-5.307e-04'
-  min: '-8.835e-02'
-  shape:
-  - 10
-  - 128
-  sum: '-6.793e-01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/resnet18_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/resnet18_cifar10_image_classifier.yaml
deleted file mode 100644
index 61ccf18e..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/resnet18_cifar10_image_classifier.yaml
+++ /dev/null
@@ -1,1017 +0,0 @@
-network.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.conv1.weight:
-  device: cuda:0
-  max: '8.688e-02'
-  mean: '5.299e-04'
-  min: '-9.862e-02'
-  shape:
-  - 64
-  - 3
-  - 7
-  - 7
-  sum: '4.986e+00'
-network.fc.bias:
-  device: cuda:0
-  max: '4.314e-02'
-  mean: '2.057e-04'
-  min: '-3.14e-02'
-  shape:
-  - 10
-  sum: '2.057e-03'
-network.fc.weight:
-  device: cuda:0
-  max: '4.418e-02'
-  mean: '1.848e-04'
-  min: '-4.414e-02'
-  shape:
-  - 10
-  - 512
-  sum: '9.461e-01'
-network.layer1.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.conv1.weight:
-  device: cuda:0
-  max: '2.433e-01'
-  mean: '1.396e-04'
-  min: '-2.501e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '5.148e+00'
-network.layer1.0.conv2.weight:
-  device: cuda:0
-  max: '2.442e-01'
-  mean: '1.259e-04'
-  min: '-2.666e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '4.642e+00'
-network.layer1.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.conv1.weight:
-  device: cuda:0
-  max: '2.456e-01'
-  mean: '1.807e-04'
-  min: '-2.376e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '6.660e+00'
-network.layer1.1.conv2.weight:
-  device: cuda:0
-  max: '2.338e-01'
-  mean: '-3.408e-04'
-  min: '-2.402e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '-1.256e+01'
-network.layer2.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.conv1.weight:
-  device: cuda:0
-  max: '1.681e-01'
-  mean: '2.319e-04'
-  min: '-1.830e-01'
-  shape:
-  - 128
-  - 64
-  - 3
-  - 3
-  sum: '1.71e+01'
-network.layer2.0.conv2.weight:
-  device: cuda:0
-  max: '2.008e-01'
-  mean: '-6.267e-05'
-  min: '-1.870e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '-9.240e+00'
-network.layer2.0.downsample.0.weight:
-  device: cuda:0
-  max: '5.180e-01'
-  mean: '-2.705e-03'
-  min: '-5.316e-01'
-  shape:
-  - 128
-  - 64
-  - 1
-  - 1
-  sum: '-2.216e+01'
-network.layer2.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.conv1.weight:
-  device: cuda:0
-  max: '1.750e-01'
-  mean: '7.981e-05'
-  min: '-1.909e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '1.177e+01'
-network.layer2.1.conv2.weight:
-  device: cuda:0
-  max: '1.714e-01'
-  mean: '6.508e-05'
-  min: '-1.811e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '9.597e+00'
-network.layer3.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.conv1.weight:
-  device: cuda:0
-  max: '1.186e-01'
-  mean: '-5.228e-06'
-  min: '-1.308e-01'
-  shape:
-  - 256
-  - 128
-  - 3
-  - 3
-  sum: '-1.542e+00'
-network.layer3.0.conv2.weight:
-  device: cuda:0
-  max: '1.360e-01'
-  mean: '-1.566e-05'
-  min: '-1.442e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '-9.235e+00'
-network.layer3.0.downsample.0.weight:
-  device: cuda:0
-  max: '4.034e-01'
-  mean: '-7.003e-06'
-  min: '-3.510e-01'
-  shape:
-  - 256
-  - 128
-  - 1
-  - 1
-  sum: '-2.295e-01'
-network.layer3.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.conv1.weight:
-  device: cuda:0
-  max: '1.435e-01'
-  mean: '1.374e-05'
-  min: '-1.476e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '8.106e+00'
-network.layer3.1.conv2.weight:
-  device: cuda:0
-  max: '1.273e-01'
-  mean: '8.978e-05'
-  min: '-1.346e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '5.295e+01'
-network.layer4.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.conv1.weight:
-  device: cuda:0
-  max: '1.020e-01'
-  mean: '-2.986e-06'
-  min: '-1.011e-01'
-  shape:
-  - 512
-  - 256
-  - 3
-  - 3
-  sum: '-3.522e+00'
-network.layer4.0.conv2.weight:
-  device: cuda:0
-  max: '1.049e-01'
-  mean: '-2.121e-05'
-  min: '-1.011e-01'
-  shape:
-  - 512
-  - 512
-  - 3
-  - 3
-  sum: '-5.004e+01'
-network.layer4.0.downsample.0.weight:
-  device: cuda:0
-  max: '2.638e-01'
-  mean: '-1.538e-05'
-  min: '-2.893e-01'
-  shape:
-  - 512
-  - 256
-  - 1
-  - 1
-  sum: '-2.016e+00'
-network.layer4.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.conv1.weight:
-  device: cuda:0
-  max: '1.056e-01'
-  mean: '4.031e-06'
-  min: '-1.011e-01'
-  shape:
-  - 512
-  - 512
-  - 3
-  - 3
-  sum: '9.511e+00'
-network.layer4.1.conv2.weight:
-  device: cuda:0
-  max: '1.072e-01'
-  mean: '-1.993e-05'
-  min: '-9.954e-02'
-  shape:
-  - 512
-  - 512
-  - 3
-  - 3
-  sum: '-4.701e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/resnet18_imagenet_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/resnet18_imagenet_image_classifier.yaml
deleted file mode 100644
index a3a1a99d..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/resnet18_imagenet_image_classifier.yaml
+++ /dev/null
@@ -1,1017 +0,0 @@
-network.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.conv1.weight:
-  device: cuda:0
-  max: '9.327e-02'
-  mean: '4.984e-04'
-  min: '-1.072e-01'
-  shape:
-  - 64
-  - 3
-  - 7
-  - 7
-  sum: '4.689e+00'
-network.fc.bias:
-  device: cuda:0
-  max: '4.419e-02'
-  mean: '1.212e-06'
-  min: '-4.419e-02'
-  shape:
-  - 1000
-  sum: '1.212e-03'
-network.fc.weight:
-  device: cuda:0
-  max: '4.419e-02'
-  mean: '-6.997e-07'
-  min: '-4.419e-02'
-  shape:
-  - 1000
-  - 512
-  sum: '-3.583e-01'
-network.layer1.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.conv1.weight:
-  device: cuda:0
-  max: '2.442e-01'
-  mean: '1.259e-04'
-  min: '-2.666e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '4.642e+00'
-network.layer1.0.conv2.weight:
-  device: cuda:0
-  max: '2.456e-01'
-  mean: '1.807e-04'
-  min: '-2.376e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '6.660e+00'
-network.layer1.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.conv1.weight:
-  device: cuda:0
-  max: '2.338e-01'
-  mean: '-3.408e-04'
-  min: '-2.402e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '-1.256e+01'
-network.layer1.1.conv2.weight:
-  device: cuda:0
-  max: '2.224e-01'
-  mean: '2.189e-04'
-  min: '-2.588e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '8.07e+00'
-network.layer2.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.conv1.weight:
-  device: cuda:0
-  max: '2.008e-01'
-  mean: '8.513e-05'
-  min: '-1.854e-01'
-  shape:
-  - 128
-  - 64
-  - 3
-  - 3
-  sum: '6.276e+00'
-network.layer2.0.conv2.weight:
-  device: cuda:0
-  max: '1.766e-01'
-  mean: '1.21e-04'
-  min: '-1.79e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '1.784e+01'
-network.layer2.0.downsample.0.weight:
-  device: cuda:0
-  max: '5.054e-01'
-  mean: '-9.048e-04'
-  min: '-4.751e-01'
-  shape:
-  - 128
-  - 64
-  - 1
-  - 1
-  sum: '-7.412e+00'
-network.layer2.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.conv1.weight:
-  device: cuda:0
-  max: '1.714e-01'
-  mean: '6.508e-05'
-  min: '-1.811e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '9.597e+00'
-network.layer2.1.conv2.weight:
-  device: cuda:0
-  max: '1.677e-01'
-  mean: '-1.988e-05'
-  min: '-1.746e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '-2.932e+00'
-network.layer3.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.conv1.weight:
-  device: cuda:0
-  max: '1.360e-01'
-  mean: '3.475e-05'
-  min: '-1.442e-01'
-  shape:
-  - 256
-  - 128
-  - 3
-  - 3
-  sum: '1.025e+01'
-network.layer3.0.conv2.weight:
-  device: cuda:0
-  max: '1.345e-01'
-  mean: '-1.856e-05'
-  min: '-1.299e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '-1.095e+01'
-network.layer3.0.downsample.0.weight:
-  device: cuda:0
-  max: '3.523e-01'
-  mean: '1.2e-04'
-  min: '-3.863e-01'
-  shape:
-  - 256
-  - 128
-  - 1
-  - 1
-  sum: '3.931e+00'
-network.layer3.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.conv1.weight:
-  device: cuda:0
-  max: '1.395e-01'
-  mean: '6.754e-05'
-  min: '-1.476e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '3.984e+01'
-network.layer3.1.conv2.weight:
-  device: cuda:0
-  max: '1.443e-01'
-  mean: '4.953e-05'
-  min: '-1.376e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '2.921e+01'
-network.layer4.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.conv1.weight:
-  device: cuda:0
-  max: '1.003e-01'
-  mean: '-1.587e-05'
-  min: '-1.011e-01'
-  shape:
-  - 512
-  - 256
-  - 3
-  - 3
-  sum: '-1.872e+01'
-network.layer4.0.conv2.weight:
-  device: cuda:0
-  max: '1.049e-01'
-  mean: '-1.442e-05'
-  min: '-1.011e-01'
-  shape:
-  - 512
-  - 512
-  - 3
-  - 3
-  sum: '-3.403e+01'
-network.layer4.0.downsample.0.weight:
-  device: cuda:0
-  max: '2.673e-01'
-  mean: '2.869e-04'
-  min: '-3.001e-01'
-  shape:
-  - 512
-  - 256
-  - 1
-  - 1
-  sum: '3.761e+01'
-network.layer4.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.conv1.weight:
-  device: cuda:0
-  max: '1.056e-01'
-  mean: '1.585e-06'
-  min: '-1.011e-01'
-  shape:
-  - 512
-  - 512
-  - 3
-  - 3
-  sum: '3.74e+00'
-network.layer4.1.conv2.weight:
-  device: cuda:0
-  max: '1.072e-01'
-  mean: '-2.285e-05'
-  min: '-1.042e-01'
-  shape:
-  - 512
-  - 512
-  - 3
-  - 3
-  sum: '-5.392e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/resnet50_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/resnet50_cifar10_image_classifier.yaml
deleted file mode 100644
index d0fb1b94..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/resnet50_cifar10_image_classifier.yaml
+++ /dev/null
@@ -1,2667 +0,0 @@
-network.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.conv1.weight:
-  device: cuda:0
-  max: '9.646e-02'
-  mean: '3.162e-04'
-  min: '-9.585e-02'
-  shape:
-  - 64
-  - 3
-  - 7
-  - 7
-  sum: '2.975e+00'
-network.fc.bias:
-  device: cuda:0
-  max: '2.199e-02'
-  mean: '3.231e-03'
-  min: '-2.176e-02'
-  shape:
-  - 10
-  sum: '3.231e-02'
-network.fc.weight:
-  device: cuda:0
-  max: '2.21e-02'
-  mean: '-7.184e-06'
-  min: '-2.21e-02'
-  shape:
-  - 10
-  - 2048
-  sum: '-1.471e-01'
-network.layer1.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.0.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.0.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.0.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.0.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.0.conv1.weight:
-  device: cuda:0
-  max: '7.081e-01'
-  mean: '-3.220e-03'
-  min: '-6.607e-01'
-  shape:
-  - 64
-  - 64
-  - 1
-  - 1
-  sum: '-1.319e+01'
-network.layer1.0.conv2.weight:
-  device: cuda:0
-  max: '2.489e-01'
-  mean: '-3.557e-04'
-  min: '-2.330e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '-1.311e+01'
-network.layer1.0.conv3.weight:
-  device: cuda:0
-  max: '3.157e-01'
-  mean: '2.669e-04'
-  min: '-3.577e-01'
-  shape:
-  - 256
-  - 64
-  - 1
-  - 1
-  sum: '4.374e+00'
-network.layer1.0.downsample.0.weight:
-  device: cuda:0
-  max: '3.370e-01'
-  mean: '4.294e-04'
-  min: '-3.389e-01'
-  shape:
-  - 256
-  - 64
-  - 1
-  - 1
-  sum: '7.036e+00'
-network.layer1.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.1.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.1.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.1.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.1.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.1.conv1.weight:
-  device: cuda:0
-  max: '7.008e-01'
-  mean: '3.792e-04'
-  min: '-6.543e-01'
-  shape:
-  - 64
-  - 256
-  - 1
-  - 1
-  sum: '6.214e+00'
-network.layer1.1.conv2.weight:
-  device: cuda:0
-  max: '2.569e-01'
-  mean: '-2.808e-06'
-  min: '-2.296e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '-1.035e-01'
-network.layer1.1.conv3.weight:
-  device: cuda:0
-  max: '3.335e-01'
-  mean: '-1.113e-03'
-  min: '-3.427e-01'
-  shape:
-  - 256
-  - 64
-  - 1
-  - 1
-  sum: '-1.824e+01'
-network.layer1.2.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.2.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.2.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.2.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.2.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.2.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.2.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.2.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.2.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.2.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.2.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.2.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.2.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.2.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.2.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.2.conv1.weight:
-  device: cuda:0
-  max: '7.078e-01'
-  mean: '2.205e-03'
-  min: '-6.688e-01'
-  shape:
-  - 64
-  - 256
-  - 1
-  - 1
-  sum: '3.613e+01'
-network.layer1.2.conv2.weight:
-  device: cuda:0
-  max: '2.568e-01'
-  mean: '2.909e-04'
-  min: '-2.361e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '1.072e+01'
-network.layer1.2.conv3.weight:
-  device: cuda:0
-  max: '3.423e-01'
-  mean: '-6.033e-04'
-  min: '-3.476e-01'
-  shape:
-  - 256
-  - 64
-  - 1
-  - 1
-  sum: '-9.884e+00'
-network.layer2.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.0.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.0.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.0.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.0.conv1.weight:
-  device: cuda:0
-  max: '5.195e-01'
-  mean: '7.903e-06'
-  min: '-5.187e-01'
-  shape:
-  - 128
-  - 256
-  - 1
-  - 1
-  sum: '2.59e-01'
-network.layer2.0.conv2.weight:
-  device: cuda:0
-  max: '1.880e-01'
-  mean: '2.495e-04'
-  min: '-1.736e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '3.678e+01'
-network.layer2.0.conv3.weight:
-  device: cuda:0
-  max: '2.546e-01'
-  mean: '2.444e-04'
-  min: '-2.541e-01'
-  shape:
-  - 512
-  - 128
-  - 1
-  - 1
-  sum: '1.602e+01'
-network.layer2.0.downsample.0.weight:
-  device: cuda:0
-  max: '3.065e-01'
-  mean: '3.991e-05'
-  min: '-2.480e-01'
-  shape:
-  - 512
-  - 256
-  - 1
-  - 1
-  sum: '5.231e+00'
-network.layer2.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.1.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.1.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.1.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.1.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.1.conv1.weight:
-  device: cuda:0
-  max: '5.655e-01'
-  mean: '-1.772e-04'
-  min: '-5.812e-01'
-  shape:
-  - 128
-  - 512
-  - 1
-  - 1
-  sum: '-1.161e+01'
-network.layer2.1.conv2.weight:
-  device: cuda:0
-  max: '1.912e-01'
-  mean: '-1.939e-04'
-  min: '-1.828e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '-2.859e+01'
-network.layer2.1.conv3.weight:
-  device: cuda:0
-  max: '2.647e-01'
-  mean: '1.202e-04'
-  min: '-2.835e-01'
-  shape:
-  - 512
-  - 128
-  - 1
-  - 1
-  sum: '7.879e+00'
-network.layer2.2.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.2.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.2.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.2.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.2.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.2.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.2.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.2.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.2.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.2.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.2.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.2.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.2.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.2.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.2.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.2.conv1.weight:
-  device: cuda:0
-  max: '5.352e-01'
-  mean: '1.514e-04'
-  min: '-4.77e-01'
-  shape:
-  - 128
-  - 512
-  - 1
-  - 1
-  sum: '9.922e+00'
-network.layer2.2.conv2.weight:
-  device: cuda:0
-  max: '1.992e-01'
-  mean: '-3.131e-05'
-  min: '-1.781e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '-4.617e+00'
-network.layer2.2.conv3.weight:
-  device: cuda:0
-  max: '3.018e-01'
-  mean: '8.808e-05'
-  min: '-2.617e-01'
-  shape:
-  - 512
-  - 128
-  - 1
-  - 1
-  sum: '5.772e+00'
-network.layer2.3.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.3.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.3.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.3.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.3.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.3.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.3.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.3.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.3.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.3.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.3.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.3.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.3.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.3.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.3.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.3.conv1.weight:
-  device: cuda:0
-  max: '5.314e-01'
-  mean: '-3.536e-04'
-  min: '-5.475e-01'
-  shape:
-  - 128
-  - 512
-  - 1
-  - 1
-  sum: '-2.318e+01'
-network.layer2.3.conv2.weight:
-  device: cuda:0
-  max: '1.754e-01'
-  mean: '7.783e-05'
-  min: '-1.808e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '1.148e+01'
-network.layer2.3.conv3.weight:
-  device: cuda:0
-  max: '2.382e-01'
-  mean: '-1.054e-05'
-  min: '-2.517e-01'
-  shape:
-  - 512
-  - 128
-  - 1
-  - 1
-  sum: '-6.906e-01'
-network.layer3.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.0.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.0.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.0.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.0.conv1.weight:
-  device: cuda:0
-  max: '3.667e-01'
-  mean: '-1.312e-04'
-  min: '-3.741e-01'
-  shape:
-  - 256
-  - 512
-  - 1
-  - 1
-  sum: '-1.72e+01'
-network.layer3.0.conv2.weight:
-  device: cuda:0
-  max: '1.525e-01'
-  mean: '3.130e-05'
-  min: '-1.458e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '1.846e+01'
-network.layer3.0.conv3.weight:
-  device: cuda:0
-  max: '2.06e-01'
-  mean: '1.398e-05'
-  min: '-2.206e-01'
-  shape:
-  - 1024
-  - 256
-  - 1
-  - 1
-  sum: '3.665e+00'
-network.layer3.0.downsample.0.weight:
-  device: cuda:0
-  max: '1.988e-01'
-  mean: '2.828e-05'
-  min: '-2.006e-01'
-  shape:
-  - 1024
-  - 512
-  - 1
-  - 1
-  sum: '1.483e+01'
-network.layer3.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.1.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.1.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.1.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.1.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.1.conv1.weight:
-  device: cuda:0
-  max: '3.843e-01'
-  mean: '2.675e-04'
-  min: '-3.99e-01'
-  shape:
-  - 256
-  - 1024
-  - 1
-  - 1
-  sum: '7.013e+01'
-network.layer3.1.conv2.weight:
-  device: cuda:0
-  max: '1.38e-01'
-  mean: '-3.53e-06'
-  min: '-1.294e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '-2.082e+00'
-network.layer3.1.conv3.weight:
-  device: cuda:0
-  max: '2.052e-01'
-  mean: '-7.496e-06'
-  min: '-1.973e-01'
-  shape:
-  - 1024
-  - 256
-  - 1
-  - 1
-  sum: '-1.965e+00'
-network.layer3.2.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.2.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.2.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.2.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.2.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.2.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.2.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.2.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.2.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.2.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.2.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.2.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.2.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.2.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.2.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.2.conv1.weight:
-  device: cuda:0
-  max: '4.040e-01'
-  mean: '5.938e-06'
-  min: '-4.109e-01'
-  shape:
-  - 256
-  - 1024
-  - 1
-  - 1
-  sum: '1.557e+00'
-network.layer3.2.conv2.weight:
-  device: cuda:0
-  max: '1.381e-01'
-  mean: '-1.49e-05'
-  min: '-1.505e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '-8.787e+00'
-network.layer3.2.conv3.weight:
-  device: cuda:0
-  max: '1.964e-01'
-  mean: '8.209e-05'
-  min: '-1.861e-01'
-  shape:
-  - 1024
-  - 256
-  - 1
-  - 1
-  sum: '2.152e+01'
-network.layer3.3.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.3.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.3.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.3.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.3.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.3.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.3.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.3.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.3.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.3.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.3.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.3.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.3.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.3.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.3.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.3.conv1.weight:
-  device: cuda:0
-  max: '3.85e-01'
-  mean: '-1.446e-04'
-  min: '-4.104e-01'
-  shape:
-  - 256
-  - 1024
-  - 1
-  - 1
-  sum: '-3.789e+01'
-network.layer3.3.conv2.weight:
-  device: cuda:0
-  max: '1.48e-01'
-  mean: '-4.522e-05'
-  min: '-1.423e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '-2.667e+01'
-network.layer3.3.conv3.weight:
-  device: cuda:0
-  max: '1.972e-01'
-  mean: '-4.765e-05'
-  min: '-2.067e-01'
-  shape:
-  - 1024
-  - 256
-  - 1
-  - 1
-  sum: '-1.249e+01'
-network.layer3.4.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.4.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.4.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.4.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.4.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.4.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.4.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.4.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.4.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.4.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.4.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.4.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.4.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.4.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.4.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.4.conv1.weight:
-  device: cuda:0
-  max: '4.356e-01'
-  mean: '9.811e-05'
-  min: '-3.892e-01'
-  shape:
-  - 256
-  - 1024
-  - 1
-  - 1
-  sum: '2.572e+01'
-network.layer3.4.conv2.weight:
-  device: cuda:0
-  max: '1.430e-01'
-  mean: '-3.322e-05'
-  min: '-1.325e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '-1.959e+01'
-network.layer3.4.conv3.weight:
-  device: cuda:0
-  max: '1.993e-01'
-  mean: '3.794e-05'
-  min: '-2.046e-01'
-  shape:
-  - 1024
-  - 256
-  - 1
-  - 1
-  sum: '9.945e+00'
-network.layer3.5.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.5.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.5.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.5.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.5.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.5.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.5.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.5.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.5.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.5.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.5.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.5.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.5.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.5.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.5.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.5.conv1.weight:
-  device: cuda:0
-  max: '4.095e-01'
-  mean: '4.100e-05'
-  min: '-3.786e-01'
-  shape:
-  - 256
-  - 1024
-  - 1
-  - 1
-  sum: '1.075e+01'
-network.layer3.5.conv2.weight:
-  device: cuda:0
-  max: '1.341e-01'
-  mean: '-1.609e-05'
-  min: '-1.361e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '-9.492e+00'
-network.layer3.5.conv3.weight:
-  device: cuda:0
-  max: '1.988e-01'
-  mean: '-1.139e-04'
-  min: '-2.040e-01'
-  shape:
-  - 1024
-  - 256
-  - 1
-  - 1
-  sum: '-2.986e+01'
-network.layer4.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.0.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.0.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.0.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.0.conv1.weight:
-  device: cuda:0
-  max: '2.970e-01'
-  mean: '5.637e-05'
-  min: '-2.903e-01'
-  shape:
-  - 512
-  - 1024
-  - 1
-  - 1
-  sum: '2.955e+01'
-network.layer4.0.conv2.weight:
-  device: cuda:0
-  max: '9.993e-02'
-  mean: '1.64e-05'
-  min: '-1.102e-01'
-  shape:
-  - 512
-  - 512
-  - 3
-  - 3
-  sum: '3.869e+01'
-network.layer4.0.conv3.weight:
-  device: cuda:0
-  max: '1.534e-01'
-  mean: '-2.382e-06'
-  min: '-1.673e-01'
-  shape:
-  - 2048
-  - 512
-  - 1
-  - 1
-  sum: '-2.498e+00'
-network.layer4.0.downsample.0.weight:
-  device: cuda:0
-  max: '1.475e-01'
-  mean: '-6.343e-06'
-  min: '-1.472e-01'
-  shape:
-  - 2048
-  - 1024
-  - 1
-  - 1
-  sum: '-1.330e+01'
-network.layer4.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.1.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.1.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.1.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.1.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.1.conv1.weight:
-  device: cuda:0
-  max: '3.285e-01'
-  mean: '5.911e-05'
-  min: '-3.033e-01'
-  shape:
-  - 512
-  - 2048
-  - 1
-  - 1
-  sum: '6.198e+01'
-network.layer4.1.conv2.weight:
-  device: cuda:0
-  max: '1.104e-01'
-  mean: '2.457e-05'
-  min: '-1.031e-01'
-  shape:
-  - 512
-  - 512
-  - 3
-  - 3
-  sum: '5.797e+01'
-network.layer4.1.conv3.weight:
-  device: cuda:0
-  max: '1.483e-01'
-  mean: '-6.445e-06'
-  min: '-1.555e-01'
-  shape:
-  - 2048
-  - 512
-  - 1
-  - 1
-  sum: '-6.758e+00'
-network.layer4.2.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.2.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.2.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.2.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.2.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.2.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.2.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.2.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.2.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.2.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.2.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.2.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.2.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.2.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.2.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.2.conv1.weight:
-  device: cuda:0
-  max: '2.960e-01'
-  mean: '-1.275e-04'
-  min: '-3.368e-01'
-  shape:
-  - 512
-  - 2048
-  - 1
-  - 1
-  sum: '-1.337e+02'
-network.layer4.2.conv2.weight:
-  device: cuda:0
-  max: '9.885e-02'
-  mean: '-6.874e-06'
-  min: '-9.988e-02'
-  shape:
-  - 512
-  - 512
-  - 3
-  - 3
-  sum: '-1.622e+01'
-network.layer4.2.conv3.weight:
-  device: cuda:0
-  max: '1.45e-01'
-  mean: '1.976e-05'
-  min: '-1.578e-01'
-  shape:
-  - 2048
-  - 512
-  - 1
-  - 1
-  sum: '2.073e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/resnet50_imagenet_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/resnet50_imagenet_image_classifier.yaml
deleted file mode 100644
index 929934db..00000000
--- a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/cuda/resnet50_imagenet_image_classifier.yaml
+++ /dev/null
@@ -1,2667 +0,0 @@
-network.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.conv1.weight:
-  device: cuda:0
-  max: '1.019e-01'
-  mean: '2.309e-04'
-  min: '-8.332e-02'
-  shape:
-  - 64
-  - 3
-  - 7
-  - 7
-  sum: '2.172e+00'
-network.fc.bias:
-  device: cuda:0
-  max: '2.203e-02'
-  mean: '4.486e-04'
-  min: '-2.206e-02'
-  shape:
-  - 1000
-  sum: '4.486e-01'
-network.fc.weight:
-  device: cuda:0
-  max: '2.21e-02'
-  mean: '6.154e-06'
-  min: '-2.21e-02'
-  shape:
-  - 1000
-  - 2048
-  sum: '1.260e+01'
-network.layer1.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.0.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.0.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.0.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.0.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.0.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.0.conv1.weight:
-  device: cuda:0
-  max: '6.509e-01'
-  mean: '1.445e-03'
-  min: '-6.027e-01'
-  shape:
-  - 64
-  - 64
-  - 1
-  - 1
-  sum: '5.919e+00'
-network.layer1.0.conv2.weight:
-  device: cuda:0
-  max: '2.359e-01'
-  mean: '1.355e-04'
-  min: '-2.49e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '4.995e+00'
-network.layer1.0.conv3.weight:
-  device: cuda:0
-  max: '3.852e-01'
-  mean: '3.642e-04'
-  min: '-3.478e-01'
-  shape:
-  - 256
-  - 64
-  - 1
-  - 1
-  sum: '5.966e+00'
-network.layer1.0.downsample.0.weight:
-  device: cuda:0
-  max: '3.423e-01'
-  mean: '-6.033e-04'
-  min: '-3.476e-01'
-  shape:
-  - 256
-  - 64
-  - 1
-  - 1
-  sum: '-9.884e+00'
-network.layer1.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.1.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.1.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.1.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.1.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.1.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.1.conv1.weight:
-  device: cuda:0
-  max: '7.347e-01'
-  mean: '1.03e-03'
-  min: '-6.643e-01'
-  shape:
-  - 64
-  - 256
-  - 1
-  - 1
-  sum: '1.687e+01'
-network.layer1.1.conv2.weight:
-  device: cuda:0
-  max: '2.614e-01'
-  mean: '3.465e-04'
-  min: '-2.217e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '1.277e+01'
-network.layer1.1.conv3.weight:
-  device: cuda:0
-  max: '3.091e-01'
-  mean: '4.206e-05'
-  min: '-3.557e-01'
-  shape:
-  - 256
-  - 64
-  - 1
-  - 1
-  sum: '6.892e-01'
-network.layer1.2.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.2.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.2.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.2.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.2.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.2.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.2.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.2.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.layer1.2.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.2.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 64
-  sum: '6.4e+01'
-network.layer1.2.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.2.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer1.2.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer1.2.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.2.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer1.2.conv1.weight:
-  device: cuda:0
-  max: '6.524e-01'
-  mean: '-1.441e-03'
-  min: '-6.990e-01'
-  shape:
-  - 64
-  - 256
-  - 1
-  - 1
-  sum: '-2.362e+01'
-network.layer1.2.conv2.weight:
-  device: cuda:0
-  max: '2.666e-01'
-  mean: '-3.895e-05'
-  min: '-2.347e-01'
-  shape:
-  - 64
-  - 64
-  - 3
-  - 3
-  sum: '-1.436e+00'
-network.layer1.2.conv3.weight:
-  device: cuda:0
-  max: '3.408e-01'
-  mean: '5.479e-04'
-  min: '-3.091e-01'
-  shape:
-  - 256
-  - 64
-  - 1
-  - 1
-  sum: '8.977e+00'
-network.layer2.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.0.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.0.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.0.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.0.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.0.conv1.weight:
-  device: cuda:0
-  max: '5.176e-01'
-  mean: '-5.491e-04'
-  min: '-4.999e-01'
-  shape:
-  - 128
-  - 256
-  - 1
-  - 1
-  sum: '-1.799e+01'
-network.layer2.0.conv2.weight:
-  device: cuda:0
-  max: '1.808e-01'
-  mean: '-1.218e-04'
-  min: '-1.887e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '-1.796e+01'
-network.layer2.0.conv3.weight:
-  device: cuda:0
-  max: '2.875e-01'
-  mean: '-1.799e-04'
-  min: '-2.593e-01'
-  shape:
-  - 512
-  - 128
-  - 1
-  - 1
-  sum: '-1.179e+01'
-network.layer2.0.downsample.0.weight:
-  device: cuda:0
-  max: '3.018e-01'
-  mean: '-5.660e-05'
-  min: '-2.697e-01'
-  shape:
-  - 512
-  - 256
-  - 1
-  - 1
-  sum: '-7.419e+00'
-network.layer2.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.1.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.1.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.1.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.1.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.1.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.1.conv1.weight:
-  device: cuda:0
-  max: '5.314e-01'
-  mean: '-3.536e-04'
-  min: '-5.475e-01'
-  shape:
-  - 128
-  - 512
-  - 1
-  - 1
-  sum: '-2.318e+01'
-network.layer2.1.conv2.weight:
-  device: cuda:0
-  max: '1.754e-01'
-  mean: '7.783e-05'
-  min: '-1.808e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '1.148e+01'
-network.layer2.1.conv3.weight:
-  device: cuda:0
-  max: '2.382e-01'
-  mean: '-1.054e-05'
-  min: '-2.517e-01'
-  shape:
-  - 512
-  - 128
-  - 1
-  - 1
-  sum: '-6.906e-01'
-network.layer2.2.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.2.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.2.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.2.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.2.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.2.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.2.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.2.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.2.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.2.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.2.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.2.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.2.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.2.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.2.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.2.conv1.weight:
-  device: cuda:0
-  max: '4.971e-01'
-  mean: '-3.09e-04'
-  min: '-5.291e-01'
-  shape:
-  - 128
-  - 512
-  - 1
-  - 1
-  sum: '-2.025e+01'
-network.layer2.2.conv2.weight:
-  device: cuda:0
-  max: '2.107e-01'
-  mean: '-7.661e-06'
-  min: '-1.779e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '-1.13e+00'
-network.layer2.2.conv3.weight:
-  device: cuda:0
-  max: '3.236e-01'
-  mean: '2.725e-05'
-  min: '-3.006e-01'
-  shape:
-  - 512
-  - 128
-  - 1
-  - 1
-  sum: '1.786e+00'
-network.layer2.3.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.3.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.3.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.3.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.3.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.3.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.3.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.3.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 128
-  sum: '0.e+00'
-network.layer2.3.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.3.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 128
-  sum: '1.28e+02'
-network.layer2.3.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.3.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer2.3.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer2.3.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.3.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer2.3.conv1.weight:
-  device: cuda:0
-  max: '5.317e-01'
-  mean: '9.857e-05'
-  min: '-5.177e-01'
-  shape:
-  - 128
-  - 512
-  - 1
-  - 1
-  sum: '6.460e+00'
-network.layer2.3.conv2.weight:
-  device: cuda:0
-  max: '1.874e-01'
-  mean: '6.223e-05'
-  min: '-1.855e-01'
-  shape:
-  - 128
-  - 128
-  - 3
-  - 3
-  sum: '9.176e+00'
-network.layer2.3.conv3.weight:
-  device: cuda:0
-  max: '2.559e-01'
-  mean: '-2.673e-04'
-  min: '-2.529e-01'
-  shape:
-  - 512
-  - 128
-  - 1
-  - 1
-  sum: '-1.752e+01'
-network.layer3.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.0.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.0.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.0.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.0.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.0.conv1.weight:
-  device: cuda:0
-  max: '3.843e-01'
-  mean: '3.586e-04'
-  min: '-3.99e-01'
-  shape:
-  - 256
-  - 512
-  - 1
-  - 1
-  sum: '4.701e+01'
-network.layer3.0.conv2.weight:
-  device: cuda:0
-  max: '1.38e-01'
-  mean: '-3.53e-06'
-  min: '-1.294e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '-2.082e+00'
-network.layer3.0.conv3.weight:
-  device: cuda:0
-  max: '2.052e-01'
-  mean: '-7.496e-06'
-  min: '-1.973e-01'
-  shape:
-  - 1024
-  - 256
-  - 1
-  - 1
-  sum: '-1.965e+00'
-network.layer3.0.downsample.0.weight:
-  device: cuda:0
-  max: '2.020e-01'
-  mean: '1.340e-05'
-  min: '-2.257e-01'
-  shape:
-  - 1024
-  - 512
-  - 1
-  - 1
-  sum: '7.027e+00'
-network.layer3.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.1.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.1.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.1.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.1.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.1.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.1.conv1.weight:
-  device: cuda:0
-  max: '4.143e-01'
-  mean: '1.499e-05'
-  min: '-3.709e-01'
-  shape:
-  - 256
-  - 1024
-  - 1
-  - 1
-  sum: '3.93e+00'
-network.layer3.1.conv2.weight:
-  device: cuda:0
-  max: '1.309e-01'
-  mean: '1.100e-05'
-  min: '-1.368e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '6.490e+00'
-network.layer3.1.conv3.weight:
-  device: cuda:0
-  max: '2.051e-01'
-  mean: '-1.367e-04'
-  min: '-1.971e-01'
-  shape:
-  - 1024
-  - 256
-  - 1
-  - 1
-  sum: '-3.584e+01'
-network.layer3.2.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.2.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.2.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.2.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.2.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.2.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.2.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.2.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.2.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.2.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.2.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.2.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.2.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.2.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.2.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.2.conv1.weight:
-  device: cuda:0
-  max: '3.993e-01'
-  mean: '-1.212e-04'
-  min: '-4.269e-01'
-  shape:
-  - 256
-  - 1024
-  - 1
-  - 1
-  sum: '-3.178e+01'
-network.layer3.2.conv2.weight:
-  device: cuda:0
-  max: '1.517e-01'
-  mean: '1.648e-05'
-  min: '-1.378e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '9.721e+00'
-network.layer3.2.conv3.weight:
-  device: cuda:0
-  max: '1.958e-01'
-  mean: '-6.993e-06'
-  min: '-1.987e-01'
-  shape:
-  - 1024
-  - 256
-  - 1
-  - 1
-  sum: '-1.833e+00'
-network.layer3.3.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.3.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.3.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.3.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.3.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.3.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.3.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.3.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.3.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.3.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.3.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.3.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.3.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.3.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.3.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.3.conv1.weight:
-  device: cuda:0
-  max: '4.290e-01'
-  mean: '-2.493e-04'
-  min: '-3.916e-01'
-  shape:
-  - 256
-  - 1024
-  - 1
-  - 1
-  sum: '-6.535e+01'
-network.layer3.3.conv2.weight:
-  device: cuda:0
-  max: '1.365e-01'
-  mean: '1.203e-05'
-  min: '-1.364e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '7.097e+00'
-network.layer3.3.conv3.weight:
-  device: cuda:0
-  max: '2.011e-01'
-  mean: '9.821e-05'
-  min: '-2.042e-01'
-  shape:
-  - 1024
-  - 256
-  - 1
-  - 1
-  sum: '2.575e+01'
-network.layer3.4.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.4.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.4.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.4.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.4.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.4.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.4.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.4.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.4.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.4.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.4.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.4.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.4.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.4.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.4.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.4.conv1.weight:
-  device: cuda:0
-  max: '3.968e-01'
-  mean: '-2.179e-04'
-  min: '-3.871e-01'
-  shape:
-  - 256
-  - 1024
-  - 1
-  - 1
-  sum: '-5.712e+01'
-network.layer3.4.conv2.weight:
-  device: cuda:0
-  max: '1.392e-01'
-  mean: '-2.276e-05'
-  min: '-1.360e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '-1.342e+01'
-network.layer3.4.conv3.weight:
-  device: cuda:0
-  max: '2.100e-01'
-  mean: '9.087e-05'
-  min: '-2.052e-01'
-  shape:
-  - 1024
-  - 256
-  - 1
-  - 1
-  sum: '2.382e+01'
-network.layer3.5.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.5.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.5.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.5.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.5.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.5.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.5.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.5.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.layer3.5.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.5.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 256
-  sum: '2.56e+02'
-network.layer3.5.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.5.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer3.5.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 1024
-  sum: '0.e+00'
-network.layer3.5.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.5.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 1024
-  sum: '1.024e+03'
-network.layer3.5.conv1.weight:
-  device: cuda:0
-  max: '3.732e-01'
-  mean: '4.573e-05'
-  min: '-4.036e-01'
-  shape:
-  - 256
-  - 1024
-  - 1
-  - 1
-  sum: '1.199e+01'
-network.layer3.5.conv2.weight:
-  device: cuda:0
-  max: '1.382e-01'
-  mean: '3.509e-05'
-  min: '-1.344e-01'
-  shape:
-  - 256
-  - 256
-  - 3
-  - 3
-  sum: '2.07e+01'
-network.layer3.5.conv3.weight:
-  device: cuda:0
-  max: '2.12e-01'
-  mean: '-2.857e-05'
-  min: '-2.015e-01'
-  shape:
-  - 1024
-  - 256
-  - 1
-  - 1
-  sum: '-7.489e+00'
-network.layer4.0.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.0.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.0.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.0.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.0.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.0.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.0.conv1.weight:
-  device: cuda:0
-  max: '2.853e-01'
-  mean: '2.027e-04'
-  min: '-2.964e-01'
-  shape:
-  - 512
-  - 1024
-  - 1
-  - 1
-  sum: '1.063e+02'
-network.layer4.0.conv2.weight:
-  device: cuda:0
-  max: '1.022e-01'
-  mean: '-7.219e-06'
-  min: '-1.115e-01'
-  shape:
-  - 512
-  - 512
-  - 3
-  - 3
-  sum: '-1.703e+01'
-network.layer4.0.conv3.weight:
-  device: cuda:0
-  max: '1.469e-01'
-  mean: '1.062e-05'
-  min: '-1.472e-01'
-  shape:
-  - 2048
-  - 512
-  - 1
-  - 1
-  sum: '1.113e+01'
-network.layer4.0.downsample.0.weight:
-  device: cuda:0
-  max: '1.643e-01'
-  mean: '1.053e-05'
-  min: '-1.525e-01'
-  shape:
-  - 2048
-  - 1024
-  - 1
-  - 1
-  sum: '2.209e+01'
-network.layer4.0.downsample.1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.0.downsample.1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.0.downsample.1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.0.downsample.1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.0.downsample.1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.1.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.1.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.1.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.1.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.1.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.1.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.1.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.1.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.1.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.1.conv1.weight:
-  device: cuda:0
-  max: '3.313e-01'
-  mean: '1.118e-04'
-  min: '-3.093e-01'
-  shape:
-  - 512
-  - 2048
-  - 1
-  - 1
-  sum: '1.172e+02'
-network.layer4.1.conv2.weight:
-  device: cuda:0
-  max: '1.056e-01'
-  mean: '-1.704e-05'
-  min: '-1.123e-01'
-  shape:
-  - 512
-  - 512
-  - 3
-  - 3
-  sum: '-4.019e+01'
-network.layer4.1.conv3.weight:
-  device: cuda:0
-  max: '1.447e-01'
-  mean: '3.966e-06'
-  min: '-1.413e-01'
-  shape:
-  - 2048
-  - 512
-  - 1
-  - 1
-  sum: '4.158e+00'
-network.layer4.2.bn1.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.2.bn1.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.2.bn1.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.2.bn1.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.2.bn1.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.2.bn2.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.2.bn2.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.2.bn2.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 512
-  sum: '0.e+00'
-network.layer4.2.bn2.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.2.bn2.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 512
-  sum: '5.12e+02'
-network.layer4.2.bn3.bias:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.2.bn3.num_batches_tracked:
-  device: cuda:0
-  max: 0
-  mean: '0.e+00'
-  min: 0
-  shape: []
-  sum: 0
-network.layer4.2.bn3.running_mean:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 2048
-  sum: '0.e+00'
-network.layer4.2.bn3.running_var:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.2.bn3.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
-  min: '1.e+00'
-  shape:
-  - 2048
-  sum: '2.048e+03'
-network.layer4.2.conv1.weight:
-  device: cuda:0
-  max: '2.966e-01'
-  mean: '-2.162e-05'
-  min: '-2.997e-01'
-  shape:
-  - 512
-  - 2048
-  - 1
-  - 1
-  sum: '-2.267e+01'
-network.layer4.2.conv2.weight:
-  device: cuda:0
-  max: '9.663e-02'
-  mean: '-1.553e-06'
-  min: '-1.052e-01'
-  shape:
-  - 512
-  - 512
-  - 3
-  - 3
-  sum: '-3.664e+00'
-network.layer4.2.conv3.weight:
-  device: cuda:0
-  max: '1.522e-01'
-  mean: '-1.257e-05'
-  min: '-1.512e-01'
-  shape:
-  - 2048
-  - 512
-  - 1
-  - 1
-  sum: '-1.318e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/fcnet_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/fcnet_cifar10_image_classifier.yaml
new file mode 100644
index 00000000..6b4b4de8
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/fcnet_cifar10_image_classifier.yaml
@@ -0,0 +1,51 @@
+network.0.1.bias:
+  device: cpu
+  max: '1.896e-02'
+  mean: '1.044e-03'
+  min: '-1.884e-02'
+  shape:
+  - 128
+  sum: '1.337e-01'
+network.0.1.weight:
+  device: cpu
+  max: '1.904e-02'
+  mean: '-1.078e-05'
+  min: '-1.904e-02'
+  shape:
+  - 128
+  - 3072
+  sum: '-4.241e+00'
+network.1.0.bias:
+  device: cpu
+  max: '8.681e-02'
+  mean: '4.204e-04'
+  min: '-8.730e-02'
+  shape:
+  - 128
+  sum: '5.381e-02'
+network.1.0.weight:
+  device: cpu
+  max: '8.937e-02'
+  mean: '-1.01e-03'
+  min: '-8.936e-02'
+  shape:
+  - 128
+  - 128
+  sum: '-1.654e+01'
+network.2.0.bias:
+  device: cpu
+  max: '8.395e-02'
+  mean: '-9.068e-04'
+  min: '-8.934e-02'
+  shape:
+  - 10
+  sum: '-9.068e-03'
+network.2.0.weight:
+  device: cpu
+  max: '8.854e-02'
+  mean: '-4.99e-04'
+  min: '-8.934e-02'
+  shape:
+  - 10
+  - 128
+  sum: '-6.387e-01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/fcnet_fashion_mnist_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/fcnet_fashion_mnist_image_classifier.yaml
new file mode 100644
index 00000000..372115b6
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/fcnet_fashion_mnist_image_classifier.yaml
@@ -0,0 +1,51 @@
+network.0.1.bias:
+  device: cpu
+  max: '3.630e-02'
+  mean: '1.200e-03'
+  min: '-3.641e-02'
+  shape:
+  - 128
+  sum: '1.536e-01'
+network.0.1.weight:
+  device: cpu
+  max: '3.671e-02'
+  mean: '8.111e-05'
+  min: '-3.671e-02'
+  shape:
+  - 128
+  - 784
+  sum: '8.140e+00'
+network.1.0.bias:
+  device: cpu
+  max: '8.168e-02'
+  mean: '-6.861e-03'
+  min: '-8.653e-02'
+  shape:
+  - 128
+  sum: '-8.782e-01'
+network.1.0.weight:
+  device: cpu
+  max: '8.937e-02'
+  mean: '1.055e-04'
+  min: '-8.938e-02'
+  shape:
+  - 128
+  - 128
+  sum: '1.728e+00'
+network.2.0.bias:
+  device: cpu
+  max: '3.938e-02'
+  mean: '-3.565e-02'
+  min: '-8.038e-02'
+  shape:
+  - 10
+  sum: '-3.565e-01'
+network.2.0.weight:
+  device: cpu
+  max: '8.929e-02'
+  mean: '-6.885e-04'
+  min: '-8.935e-02'
+  shape:
+  - 10
+  - 128
+  sum: '-8.813e-01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/fcnet_mnist_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/fcnet_mnist_image_classifier.yaml
new file mode 100644
index 00000000..7f3227d2
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/fcnet_mnist_image_classifier.yaml
@@ -0,0 +1,51 @@
+network.0.1.bias:
+  device: cpu
+  max: '3.630e-02'
+  mean: '1.357e-03'
+  min: '-3.509e-02'
+  shape:
+  - 128
+  sum: '1.736e-01'
+network.0.1.weight:
+  device: cpu
+  max: '3.671e-02'
+  mean: '7.046e-05'
+  min: '-3.671e-02'
+  shape:
+  - 128
+  - 784
+  sum: '7.070e+00'
+network.1.0.bias:
+  device: cpu
+  max: '8.321e-02'
+  mean: '-6.689e-03'
+  min: '-8.653e-02'
+  shape:
+  - 128
+  sum: '-8.562e-01'
+network.1.0.weight:
+  device: cpu
+  max: '8.935e-02'
+  mean: '1.302e-04'
+  min: '-8.938e-02'
+  shape:
+  - 128
+  - 128
+  sum: '2.134e+00'
+network.2.0.bias:
+  device: cpu
+  max: '4.138e-02'
+  mean: '-3.545e-02'
+  min: '-8.038e-02'
+  shape:
+  - 10
+  sum: '-3.545e-01'
+network.2.0.weight:
+  device: cpu
+  max: '8.929e-02'
+  mean: '-6.76e-04'
+  min: '-8.917e-02'
+  shape:
+  - 10
+  - 128
+  sum: '-8.652e-01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/resnet18_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/resnet18_cifar10_image_classifier.yaml
new file mode 100644
index 00000000..29bebfd2
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/resnet18_cifar10_image_classifier.yaml
@@ -0,0 +1,1017 @@
+network.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.125e-05'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '-2.e-03'
+network.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.bn1.running_mean:
+  device: cpu
+  max: '8.872e-04'
+  mean: '-5.389e-05'
+  min: '-1.323e-03'
+  shape:
+  - 64
+  sum: '-3.449e-03'
+network.bn1.running_var:
+  device: cpu
+  max: '9.357e-01'
+  mean: '9.094e-01'
+  min: '9.016e-01'
+  shape:
+  - 64
+  sum: '5.820e+01'
+network.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.998e-01'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.399e+01'
+network.conv1.weight:
+  device: cpu
+  max: '8.788e-02'
+  mean: '5.227e-04'
+  min: '-9.962e-02'
+  shape:
+  - 64
+  - 3
+  - 7
+  - 7
+  sum: '4.918e+00'
+network.fc.bias:
+  device: cpu
+  max: '4.414e-02'
+  mean: '2.057e-04'
+  min: '-3.04e-02'
+  shape:
+  - 10
+  sum: '2.057e-03'
+network.fc.weight:
+  device: cpu
+  max: '4.518e-02'
+  mean: '2.801e-04'
+  min: '-4.511e-02'
+  shape:
+  - 10
+  - 512
+  sum: '1.434e+00'
+network.layer1.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '6.250e-05'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '4.000e-03'
+network.layer1.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.0.bn1.running_mean:
+  device: cpu
+  max: '3.021e-01'
+  mean: '7.592e-03'
+  min: '-2.47e-01'
+  shape:
+  - 64
+  sum: '4.859e-01'
+network.layer1.0.bn1.running_var:
+  device: cpu
+  max: '1.281e+00'
+  mean: '1.023e+00'
+  min: '9.514e-01'
+  shape:
+  - 64
+  sum: '6.550e+01'
+network.layer1.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.401e+01'
+network.layer1.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '3.471e-09'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '2.221e-07'
+network.layer1.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.0.bn2.running_mean:
+  device: cpu
+  max: '1.451e-01'
+  mean: '1.133e-03'
+  min: '-8.271e-02'
+  shape:
+  - 64
+  sum: '7.249e-02'
+network.layer1.0.bn2.running_var:
+  device: cpu
+  max: '1.002e+00'
+  mean: '9.616e-01'
+  min: '9.357e-01'
+  shape:
+  - 64
+  sum: '6.154e+01'
+network.layer1.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.399e+01'
+network.layer1.0.conv1.weight:
+  device: cpu
+  max: '2.443e-01'
+  mean: '1.255e-04'
+  min: '-2.511e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '4.626e+00'
+network.layer1.0.conv2.weight:
+  device: cpu
+  max: '2.452e-01'
+  mean: '1.129e-04'
+  min: '-2.676e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '4.164e+00'
+network.layer1.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '3.238e-09'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '2.072e-07'
+network.layer1.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.1.bn1.running_mean:
+  device: cpu
+  max: '1.964e-01'
+  mean: '1.232e-02'
+  min: '-2.88e-01'
+  shape:
+  - 64
+  sum: '7.882e-01'
+network.layer1.1.bn1.running_var:
+  device: cpu
+  max: '1.287e+00'
+  mean: '1.115e+00'
+  min: '1.025e+00'
+  shape:
+  - 64
+  sum: '7.136e+01'
+network.layer1.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.400e+01'
+network.layer1.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-9.378e-05'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '-6.002e-03'
+network.layer1.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.1.bn2.running_mean:
+  device: cpu
+  max: '9.202e-02'
+  mean: '-6.676e-03'
+  min: '-1.370e-01'
+  shape:
+  - 64
+  sum: '-4.273e-01'
+network.layer1.1.bn2.running_var:
+  device: cpu
+  max: '9.994e-01'
+  mean: '9.636e-01'
+  min: '9.458e-01'
+  shape:
+  - 64
+  sum: '6.167e+01'
+network.layer1.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.401e+01'
+network.layer1.1.conv1.weight:
+  device: cpu
+  max: '2.446e-01'
+  mean: '1.939e-04'
+  min: '-2.386e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '7.146e+00'
+network.layer1.1.conv2.weight:
+  device: cpu
+  max: '2.348e-01'
+  mean: '-3.617e-04'
+  min: '-2.412e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '-1.333e+01'
+network.layer2.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.125e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-4.e-03'
+network.layer2.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.bn1.running_mean:
+  device: cpu
+  max: '2.523e-01'
+  mean: '1.447e-02'
+  min: '-2.964e-01'
+  shape:
+  - 128
+  sum: '1.852e+00'
+network.layer2.0.bn1.running_var:
+  device: cpu
+  max: '1.207e+00'
+  mean: '1.054e+00'
+  min: '9.876e-01'
+  shape:
+  - 128
+  sum: '1.349e+02'
+network.layer2.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.28e+02'
+network.layer2.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.011e-09'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-1.295e-07'
+network.layer2.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.bn2.running_mean:
+  device: cpu
+  max: '9.008e-02'
+  mean: '-1.762e-03'
+  min: '-1.122e-01'
+  shape:
+  - 128
+  sum: '-2.255e-01'
+network.layer2.0.bn2.running_var:
+  device: cpu
+  max: '1.008e+00'
+  mean: '9.590e-01'
+  min: '9.383e-01'
+  shape:
+  - 128
+  sum: '1.228e+02'
+network.layer2.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.0.conv1.weight:
+  device: cpu
+  max: '1.671e-01'
+  mean: '2.672e-04'
+  min: '-1.840e-01'
+  shape:
+  - 128
+  - 64
+  - 3
+  - 3
+  sum: '1.97e+01'
+network.layer2.0.conv2.weight:
+  device: cpu
+  max: '2.018e-01'
+  mean: '-9.244e-05'
+  min: '-1.880e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '-1.363e+01'
+network.layer2.0.downsample.0.weight:
+  device: cpu
+  max: '5.170e-01'
+  mean: '-2.743e-03'
+  min: '-5.326e-01'
+  shape:
+  - 128
+  - 64
+  - 1
+  - 1
+  sum: '-2.247e+01'
+network.layer2.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.011e-09'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-1.295e-07'
+network.layer2.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.downsample.1.running_mean:
+  device: cpu
+  max: '3.096e-01'
+  mean: '-1.969e-02'
+  min: '-3.768e-01'
+  shape:
+  - 128
+  sum: '-2.52e+00'
+network.layer2.0.downsample.1.running_var:
+  device: cpu
+  max: '1.21e+00'
+  mean: '1.053e+00'
+  min: '9.69e-01'
+  shape:
+  - 128
+  sum: '1.348e+02'
+network.layer2.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.125e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-4.000e-03'
+network.layer2.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.1.bn1.running_mean:
+  device: cpu
+  max: '1.27e-01'
+  mean: '3.568e-03'
+  min: '-1.194e-01'
+  shape:
+  - 128
+  sum: '4.567e-01'
+network.layer2.1.bn1.running_var:
+  device: cpu
+  max: '1.088e+00'
+  mean: '1.015e+00'
+  min: '9.81e-01'
+  shape:
+  - 128
+  sum: '1.3e+02'
+network.layer2.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.28e+02'
+network.layer2.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-4.687e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-6.e-03'
+network.layer2.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.1.bn2.running_mean:
+  device: cpu
+  max: '1.097e-01'
+  mean: '1.123e-03'
+  min: '-1.121e-01'
+  shape:
+  - 128
+  sum: '1.437e-01'
+network.layer2.1.bn2.running_var:
+  device: cpu
+  max: '9.986e-01'
+  mean: '9.605e-01'
+  min: '9.406e-01'
+  shape:
+  - 128
+  sum: '1.229e+02'
+network.layer2.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.1.conv1.weight:
+  device: cpu
+  max: '1.740e-01'
+  mean: '7.340e-05'
+  min: '-1.919e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '1.082e+01'
+network.layer2.1.conv2.weight:
+  device: cpu
+  max: '1.724e-01'
+  mean: '5.159e-05'
+  min: '-1.801e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '7.607e+00'
+network.layer3.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '4.692e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '1.201e-02'
+network.layer3.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.bn1.running_mean:
+  device: cpu
+  max: '1.503e-01'
+  mean: '-1.008e-03'
+  min: '-1.904e-01'
+  shape:
+  - 256
+  sum: '-2.581e-01'
+network.layer3.0.bn1.running_var:
+  device: cpu
+  max: '1.082e+00'
+  mean: '9.841e-01'
+  min: '9.478e-01'
+  shape:
+  - 256
+  sum: '2.519e+02'
+network.layer3.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '6.252e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '1.600e-02'
+network.layer3.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.bn2.running_mean:
+  device: cpu
+  max: '8.791e-02'
+  mean: '1.907e-03'
+  min: '-8.55e-02'
+  shape:
+  - 256
+  sum: '4.882e-01'
+network.layer3.0.bn2.running_var:
+  device: cpu
+  max: '9.825e-01'
+  mean: '9.367e-01'
+  min: '9.196e-01'
+  shape:
+  - 256
+  sum: '2.398e+02'
+network.layer3.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.0.conv1.weight:
+  device: cpu
+  max: '1.196e-01'
+  mean: '1.466e-06'
+  min: '-1.298e-01'
+  shape:
+  - 256
+  - 128
+  - 3
+  - 3
+  sum: '4.325e-01'
+network.layer3.0.conv2.weight:
+  device: cpu
+  max: '1.350e-01'
+  mean: '-1.058e-05'
+  min: '-1.452e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '-6.239e+00'
+network.layer3.0.downsample.0.weight:
+  device: cpu
+  max: '4.024e-01'
+  mean: '-1.054e-05'
+  min: '-3.520e-01'
+  shape:
+  - 256
+  - 128
+  - 1
+  - 1
+  sum: '-3.455e-01'
+network.layer3.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '6.252e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '1.600e-02'
+network.layer3.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.downsample.1.running_mean:
+  device: cpu
+  max: '2.051e-01'
+  mean: '-7.024e-04'
+  min: '-3.726e-01'
+  shape:
+  - 256
+  sum: '-1.798e-01'
+network.layer3.0.downsample.1.running_var:
+  device: cpu
+  max: '1.212e+00'
+  mean: '9.926e-01'
+  min: '9.517e-01'
+  shape:
+  - 256
+  sum: '2.541e+02'
+network.layer3.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-7.843e-06'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-2.008e-03'
+network.layer3.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.1.bn1.running_mean:
+  device: cpu
+  max: '9.675e-02'
+  mean: '-2.579e-05'
+  min: '-1.294e-01'
+  shape:
+  - 256
+  sum: '-6.603e-03'
+network.layer3.1.bn1.running_var:
+  device: cpu
+  max: '1.057e+00'
+  mean: '9.746e-01'
+  min: '9.456e-01'
+  shape:
+  - 256
+  sum: '2.495e+02'
+network.layer3.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '1.563e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '4.000e-03'
+network.layer3.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.1.bn2.running_mean:
+  device: cpu
+  max: '9.331e-02'
+  mean: '3.957e-03'
+  min: '-1.214e-01'
+  shape:
+  - 256
+  sum: '1.013e+00'
+network.layer3.1.bn2.running_var:
+  device: cpu
+  max: '9.810e-01'
+  mean: '9.357e-01'
+  min: '9.216e-01'
+  shape:
+  - 256
+  sum: '2.395e+02'
+network.layer3.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.1.conv1.weight:
+  device: cpu
+  max: '1.445e-01'
+  mean: '1.573e-05'
+  min: '-1.466e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '9.280e+00'
+network.layer3.1.conv2.weight:
+  device: cpu
+  max: '1.267e-01'
+  mean: '8.883e-05'
+  min: '-1.336e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '5.239e+01'
+network.layer4.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '7.812e-06'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '4.e-03'
+network.layer4.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.bn1.running_mean:
+  device: cpu
+  max: '1.934e-01'
+  mean: '5.216e-04'
+  min: '-1.702e-01'
+  shape:
+  - 512
+  sum: '2.670e-01'
+network.layer4.0.bn1.running_var:
+  device: cpu
+  max: '9.776e-01'
+  mean: '9.378e-01'
+  min: '9.185e-01'
+  shape:
+  - 512
+  sum: '4.802e+02'
+network.layer4.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.445e-04'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-7.4e-02'
+network.layer4.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.bn2.running_mean:
+  device: cpu
+  max: '4.278e-02'
+  mean: '-5.827e-04'
+  min: '-5.497e-02'
+  shape:
+  - 512
+  sum: '-2.983e-01'
+network.layer4.0.bn2.running_var:
+  device: cpu
+  max: '9.17e-01'
+  mean: '9.076e-01'
+  min: '9.042e-01'
+  shape:
+  - 512
+  sum: '4.647e+02'
+network.layer4.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.998e-01'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.119e+02'
+network.layer4.0.conv1.weight:
+  device: cpu
+  max: '1.020e-01'
+  mean: '3.120e-06'
+  min: '-1.021e-01'
+  shape:
+  - 512
+  - 256
+  - 3
+  - 3
+  sum: '3.681e+00'
+network.layer4.0.conv2.weight:
+  device: cpu
+  max: '1.049e-01'
+  mean: '-2.305e-05'
+  min: '-1.011e-01'
+  shape:
+  - 512
+  - 512
+  - 3
+  - 3
+  sum: '-5.439e+01'
+network.layer4.0.downsample.0.weight:
+  device: cpu
+  max: '2.628e-01'
+  mean: '-3.184e-05'
+  min: '-2.883e-01'
+  shape:
+  - 512
+  - 256
+  - 1
+  - 1
+  sum: '-4.174e+00'
+network.layer4.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.445e-04'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-7.4e-02'
+network.layer4.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.downsample.1.running_mean:
+  device: cpu
+  max: '2.743e-01'
+  mean: '-4.804e-03'
+  min: '-3.517e-01'
+  shape:
+  - 512
+  sum: '-2.46e+00'
+network.layer4.0.downsample.1.running_var:
+  device: cpu
+  max: '1.062e+00'
+  mean: '9.75e-01'
+  min: '9.372e-01'
+  shape:
+  - 512
+  sum: '4.992e+02'
+network.layer4.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.182e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-6.054e-03'
+network.layer4.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.1.bn1.running_mean:
+  device: cpu
+  max: '7.927e-02'
+  mean: '1.359e-03'
+  min: '-6.822e-02'
+  shape:
+  - 512
+  sum: '6.956e-01'
+network.layer4.1.bn1.running_var:
+  device: cpu
+  max: '9.301e-01'
+  mean: '9.153e-01'
+  min: '9.07e-01'
+  shape:
+  - 512
+  sum: '4.686e+02'
+network.layer4.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.797e-04'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-9.201e-02'
+network.layer4.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.1.bn2.running_mean:
+  device: cpu
+  max: '5.706e-02'
+  mean: '-8.143e-05'
+  min: '-5.749e-02'
+  shape:
+  - 512
+  sum: '-4.169e-02'
+network.layer4.1.bn2.running_var:
+  device: cpu
+  max: '9.144e-01'
+  mean: '9.078e-01'
+  min: '9.04e-01'
+  shape:
+  - 512
+  sum: '4.648e+02'
+network.layer4.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.119e+02'
+network.layer4.1.conv1.weight:
+  device: cpu
+  max: '1.066e-01'
+  mean: '4.400e-06'
+  min: '-1.011e-01'
+  shape:
+  - 512
+  - 512
+  - 3
+  - 3
+  sum: '1.038e+01'
+network.layer4.1.conv2.weight:
+  device: cpu
+  max: '1.072e-01'
+  mean: '-2.072e-05'
+  min: '-9.954e-02'
+  shape:
+  - 512
+  - 512
+  - 3
+  - 3
+  sum: '-4.889e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/resnet18_imagenet_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/resnet18_imagenet_image_classifier.yaml
new file mode 100644
index 00000000..a3a9aee4
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/resnet18_imagenet_image_classifier.yaml
@@ -0,0 +1,1017 @@
+network.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '2.947e-10'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '1.886e-08'
+network.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.bn1.running_mean:
+  device: cpu
+  max: '3.233e-03'
+  mean: '-4.277e-04'
+  min: '-6.195e-03'
+  shape:
+  - 64
+  sum: '-2.737e-02'
+network.bn1.running_var:
+  device: cpu
+  max: '1.017e+00'
+  mean: '9.157e-01'
+  min: '9.017e-01'
+  shape:
+  - 64
+  sum: '5.861e+01'
+network.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.4e+01'
+network.conv1.weight:
+  device: cpu
+  max: '9.427e-02'
+  mean: '4.244e-04'
+  min: '-1.082e-01'
+  shape:
+  - 64
+  - 3
+  - 7
+  - 7
+  sum: '3.993e+00'
+network.fc.bias:
+  device: cpu
+  max: '4.325e-02'
+  mean: '-8.748e-04'
+  min: '-4.519e-02'
+  shape:
+  - 1000
+  sum: '-8.748e-01'
+network.fc.weight:
+  device: cpu
+  max: '4.519e-02'
+  mean: '-8.767e-04'
+  min: '-4.519e-02'
+  shape:
+  - 1000
+  - 512
+  sum: '-4.489e+02'
+network.layer1.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '9.375e-05'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '6.000e-03'
+network.layer1.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.0.bn1.running_mean:
+  device: cpu
+  max: '2.821e-01'
+  mean: '1.067e-03'
+  min: '-1.893e-01'
+  shape:
+  - 64
+  sum: '6.828e-02'
+network.layer1.0.bn1.running_var:
+  device: cpu
+  max: '1.207e+00'
+  mean: '9.955e-01'
+  min: '9.245e-01'
+  shape:
+  - 64
+  sum: '6.371e+01'
+network.layer1.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.401e+01'
+network.layer1.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-6.25e-05'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '-4.e-03'
+network.layer1.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.0.bn2.running_mean:
+  device: cpu
+  max: '8.859e-02'
+  mean: '4.093e-03'
+  min: '-1.145e-01'
+  shape:
+  - 64
+  sum: '2.619e-01'
+network.layer1.0.bn2.running_var:
+  device: cpu
+  max: '1.037e+00'
+  mean: '9.629e-01'
+  min: '9.286e-01'
+  shape:
+  - 64
+  sum: '6.162e+01'
+network.layer1.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.399e+01'
+network.layer1.0.conv1.weight:
+  device: cpu
+  max: '2.452e-01'
+  mean: '1.326e-04'
+  min: '-2.676e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '4.89e+00'
+network.layer1.0.conv2.weight:
+  device: cpu
+  max: '2.466e-01'
+  mean: '1.615e-04'
+  min: '-2.386e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '5.955e+00'
+network.layer1.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '2.5e-04'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '1.6e-02'
+network.layer1.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.1.bn1.running_mean:
+  device: cpu
+  max: '2.318e-01'
+  mean: '-1.281e-02'
+  min: '-3.239e-01'
+  shape:
+  - 64
+  sum: '-8.200e-01'
+network.layer1.1.bn1.running_var:
+  device: cpu
+  max: '1.242e+00'
+  mean: '1.08e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.911e+01'
+network.layer1.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.4e+01'
+network.layer1.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '3.125e-05'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '2.e-03'
+network.layer1.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.1.bn2.running_mean:
+  device: cpu
+  max: '1.357e-01'
+  mean: '4.709e-03'
+  min: '-1.103e-01'
+  shape:
+  - 64
+  sum: '3.014e-01'
+network.layer1.1.bn2.running_var:
+  device: cpu
+  max: '1.046e+00'
+  mean: '9.718e-01'
+  min: '9.465e-01'
+  shape:
+  - 64
+  sum: '6.22e+01'
+network.layer1.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.400e+01'
+network.layer1.1.conv1.weight:
+  device: cpu
+  max: '2.348e-01'
+  mean: '-3.259e-04'
+  min: '-2.412e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '-1.201e+01'
+network.layer1.1.conv2.weight:
+  device: cpu
+  max: '2.214e-01'
+  mean: '2.134e-04'
+  min: '-2.578e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '7.868e+00'
+network.layer2.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.563e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-2.001e-03'
+network.layer2.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.bn1.running_mean:
+  device: cpu
+  max: '3.061e-01'
+  mean: '6.256e-03'
+  min: '-2.212e-01'
+  shape:
+  - 128
+  sum: '8.007e-01'
+network.layer2.0.bn1.running_var:
+  device: cpu
+  max: '1.228e+00'
+  mean: '1.043e+00'
+  min: '9.757e-01'
+  shape:
+  - 128
+  sum: '1.336e+02'
+network.layer2.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.28e+02'
+network.layer2.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '4.608e-09'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '5.898e-07'
+network.layer2.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.bn2.running_mean:
+  device: cpu
+  max: '1.426e-01'
+  mean: '5.192e-03'
+  min: '-1.142e-01'
+  shape:
+  - 128
+  sum: '6.646e-01'
+network.layer2.0.bn2.running_var:
+  device: cpu
+  max: '1.107e+00'
+  mean: '9.722e-01'
+  min: '9.448e-01'
+  shape:
+  - 128
+  sum: '1.244e+02'
+network.layer2.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.0.conv1.weight:
+  device: cpu
+  max: '1.998e-01'
+  mean: '7.805e-05'
+  min: '-1.864e-01'
+  shape:
+  - 128
+  - 64
+  - 3
+  - 3
+  sum: '5.755e+00'
+network.layer2.0.conv2.weight:
+  device: cpu
+  max: '1.776e-01'
+  mean: '1.351e-04'
+  min: '-1.8e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '1.992e+01'
+network.layer2.0.downsample.0.weight:
+  device: cpu
+  max: '5.064e-01'
+  mean: '-8.682e-04'
+  min: '-4.761e-01'
+  shape:
+  - 128
+  - 64
+  - 1
+  - 1
+  sum: '-7.113e+00'
+network.layer2.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '4.608e-09'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '5.898e-07'
+network.layer2.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.downsample.1.running_mean:
+  device: cpu
+  max: '3.693e-01'
+  mean: '-6.929e-03'
+  min: '-4.204e-01'
+  shape:
+  - 128
+  sum: '-8.869e-01'
+network.layer2.0.downsample.1.running_var:
+  device: cpu
+  max: '1.56e+00'
+  mean: '1.049e+00'
+  min: '9.511e-01'
+  shape:
+  - 128
+  sum: '1.342e+02'
+network.layer2.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.28e+02'
+network.layer2.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.562e-04'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-2.e-02'
+network.layer2.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.1.bn1.running_mean:
+  device: cpu
+  max: '1.797e-01'
+  mean: '4.352e-03'
+  min: '-2.223e-01'
+  shape:
+  - 128
+  sum: '5.571e-01'
+network.layer2.1.bn1.running_var:
+  device: cpu
+  max: '1.189e+00'
+  mean: '1.044e+00'
+  min: '9.926e-01'
+  shape:
+  - 128
+  sum: '1.336e+02'
+network.layer2.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-4.695e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-6.010e-03'
+network.layer2.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.1.bn2.running_mean:
+  device: cpu
+  max: '1.176e-01'
+  mean: '-6.328e-04'
+  min: '-1.186e-01'
+  shape:
+  - 128
+  sum: '-8.100e-02'
+network.layer2.1.bn2.running_var:
+  device: cpu
+  max: '1.022e+00'
+  mean: '9.706e-01'
+  min: '9.518e-01'
+  shape:
+  - 128
+  sum: '1.242e+02'
+network.layer2.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.1.conv1.weight:
+  device: cpu
+  max: '1.704e-01'
+  mean: '6.505e-05'
+  min: '-1.821e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '9.593e+00'
+network.layer2.1.conv2.weight:
+  device: cpu
+  max: '1.667e-01'
+  mean: '-2.992e-06'
+  min: '-1.74e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '-4.412e-01'
+network.layer3.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '1.561e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '3.995e-03'
+network.layer3.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.bn1.running_mean:
+  device: cpu
+  max: '2.462e-01'
+  mean: '3.698e-03'
+  min: '-1.822e-01'
+  shape:
+  - 256
+  sum: '9.467e-01'
+network.layer3.0.bn1.running_var:
+  device: cpu
+  max: '1.11e+00'
+  mean: '1.007e+00'
+  min: '9.700e-01'
+  shape:
+  - 256
+  sum: '2.577e+02'
+network.layer3.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-8.601e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-2.202e-02'
+network.layer3.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.bn2.running_mean:
+  device: cpu
+  max: '1.179e-01'
+  mean: '-1.227e-03'
+  min: '-1.495e-01'
+  shape:
+  - 256
+  sum: '-3.142e-01'
+network.layer3.0.bn2.running_var:
+  device: cpu
+  max: '1.019e+00'
+  mean: '9.675e-01'
+  min: '9.505e-01'
+  shape:
+  - 256
+  sum: '2.477e+02'
+network.layer3.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.0.conv1.weight:
+  device: cpu
+  max: '1.350e-01'
+  mean: '4.386e-05'
+  min: '-1.452e-01'
+  shape:
+  - 256
+  - 128
+  - 3
+  - 3
+  sum: '1.294e+01'
+network.layer3.0.conv2.weight:
+  device: cpu
+  max: '1.336e-01'
+  mean: '-2.709e-05'
+  min: '-1.289e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '-1.598e+01'
+network.layer3.0.downsample.0.weight:
+  device: cpu
+  max: '3.533e-01'
+  mean: '1.033e-04'
+  min: '-3.873e-01'
+  shape:
+  - 256
+  - 128
+  - 1
+  - 1
+  sum: '3.385e+00'
+network.layer3.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-8.601e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-2.202e-02'
+network.layer3.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.downsample.1.running_mean:
+  device: cpu
+  max: '2.248e-01'
+  mean: '1.547e-03'
+  min: '-2.048e-01'
+  shape:
+  - 256
+  sum: '3.96e-01'
+network.layer3.0.downsample.1.running_var:
+  device: cpu
+  max: '1.107e+00'
+  mean: '1.004e+00'
+  min: '9.547e-01'
+  shape:
+  - 256
+  sum: '2.571e+02'
+network.layer3.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-7.818e-06'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-2.001e-03'
+network.layer3.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.1.bn1.running_mean:
+  device: cpu
+  max: '2.06e-01'
+  mean: '7.639e-03'
+  min: '-1.81e-01'
+  shape:
+  - 256
+  sum: '1.956e+00'
+network.layer3.1.bn1.running_var:
+  device: cpu
+  max: '1.163e+00'
+  mean: '1.037e+00'
+  min: '1.003e+00'
+  shape:
+  - 256
+  sum: '2.655e+02'
+network.layer3.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.019e-04'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-2.61e-02'
+network.layer3.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.1.bn2.running_mean:
+  device: cpu
+  max: '1.548e-01'
+  mean: '3.756e-03'
+  min: '-1.539e-01'
+  shape:
+  - 256
+  sum: '9.615e-01'
+network.layer3.1.bn2.running_var:
+  device: cpu
+  max: '1.016e+00'
+  mean: '9.688e-01'
+  min: '9.546e-01'
+  shape:
+  - 256
+  sum: '2.480e+02'
+network.layer3.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.1.conv1.weight:
+  device: cpu
+  max: '1.385e-01'
+  mean: '5.855e-05'
+  min: '-1.486e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '3.453e+01'
+network.layer3.1.conv2.weight:
+  device: cpu
+  max: '1.433e-01'
+  mean: '6.613e-05'
+  min: '-1.386e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '3.901e+01'
+network.layer4.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-7.023e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-3.596e-02'
+network.layer4.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.bn1.running_mean:
+  device: cpu
+  max: '1.898e-01'
+  mean: '-2.48e-03'
+  min: '-2.44e-01'
+  shape:
+  - 512
+  sum: '-1.27e+00'
+network.layer4.0.bn1.running_var:
+  device: cpu
+  max: '1.117e+00'
+  mean: '1.006e+00'
+  min: '9.755e-01'
+  shape:
+  - 512
+  sum: '5.150e+02'
+network.layer4.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.133e-04'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-5.801e-02'
+network.layer4.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.bn2.running_mean:
+  device: cpu
+  max: '1.393e-01'
+  mean: '-2.050e-03'
+  min: '-1.41e-01'
+  shape:
+  - 512
+  sum: '-1.05e+00'
+network.layer4.0.bn2.running_var:
+  device: cpu
+  max: '1.005e+00'
+  mean: '9.634e-01'
+  min: '9.486e-01'
+  shape:
+  - 512
+  sum: '4.933e+02'
+network.layer4.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.119e+02'
+network.layer4.0.conv1.weight:
+  device: cpu
+  max: '1.013e-01'
+  mean: '-6.655e-06'
+  min: '-1.021e-01'
+  shape:
+  - 512
+  - 256
+  - 3
+  - 3
+  sum: '-7.850e+00'
+network.layer4.0.conv2.weight:
+  device: cpu
+  max: '1.059e-01'
+  mean: '-1.76e-05'
+  min: '-1.001e-01'
+  shape:
+  - 512
+  - 512
+  - 3
+  - 3
+  sum: '-4.152e+01'
+network.layer4.0.downsample.0.weight:
+  device: cpu
+  max: '2.683e-01'
+  mean: '2.762e-04'
+  min: '-2.991e-01'
+  shape:
+  - 512
+  - 256
+  - 1
+  - 1
+  sum: '3.620e+01'
+network.layer4.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.133e-04'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-5.801e-02'
+network.layer4.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.downsample.1.running_mean:
+  device: cpu
+  max: '2.44e-01'
+  mean: '5.689e-03'
+  min: '-2.274e-01'
+  shape:
+  - 512
+  sum: '2.913e+00'
+network.layer4.0.downsample.1.running_var:
+  device: cpu
+  max: '1.188e+00'
+  mean: '1.007e+00'
+  min: '9.656e-01'
+  shape:
+  - 512
+  sum: '5.154e+02'
+network.layer4.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.119e+02'
+network.layer4.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-7.583e-06'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-3.883e-03'
+network.layer4.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.1.bn1.running_mean:
+  device: cpu
+  max: '1.906e-01'
+  mean: '3.186e-04'
+  min: '-1.807e-01'
+  shape:
+  - 512
+  sum: '1.631e-01'
+network.layer4.1.bn1.running_var:
+  device: cpu
+  max: '1.137e+00'
+  mean: '1.030e+00'
+  min: '1.000e+00'
+  shape:
+  - 512
+  sum: '5.275e+02'
+network.layer4.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.562e-04'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-8.e-02'
+network.layer4.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.1.bn2.running_mean:
+  device: cpu
+  max: '1.683e-01'
+  mean: '-3.438e-03'
+  min: '-1.793e-01'
+  shape:
+  - 512
+  sum: '-1.760e+00'
+network.layer4.1.bn2.running_var:
+  device: cpu
+  max: '1.058e+00'
+  mean: '9.656e-01'
+  min: '9.492e-01'
+  shape:
+  - 512
+  sum: '4.944e+02'
+network.layer4.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.119e+02'
+network.layer4.1.conv1.weight:
+  device: cpu
+  max: '1.046e-01'
+  mean: '9.568e-06'
+  min: '-1.021e-01'
+  shape:
+  - 512
+  - 512
+  - 3
+  - 3
+  sum: '2.257e+01'
+network.layer4.1.conv2.weight:
+  device: cpu
+  max: '1.062e-01'
+  mean: '-3.053e-05'
+  min: '-1.052e-01'
+  shape:
+  - 512
+  - 512
+  - 3
+  - 3
+  sum: '-7.202e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/resnet50_cifar10_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/resnet50_cifar10_image_classifier.yaml
new file mode 100644
index 00000000..1825c2a4
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/resnet50_cifar10_image_classifier.yaml
@@ -0,0 +1,2667 @@
+network.bn1.bias:
+  device: cpu
+  max: '1.000e-03'
+  mean: '-3.125e-05'
+  min: '-1.000e-03'
+  shape:
+  - 64
+  sum: '-2.000e-03'
+network.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.bn1.running_mean:
+  device: cpu
+  max: '1.155e-03'
+  mean: '1.054e-06'
+  min: '-9.642e-04'
+  shape:
+  - 64
+  sum: '6.748e-05'
+network.bn1.running_var:
+  device: cpu
+  max: '9.614e-01'
+  mean: '9.1e-01'
+  min: '9.020e-01'
+  shape:
+  - 64
+  sum: '5.824e+01'
+network.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.400e+01'
+network.conv1.weight:
+  device: cpu
+  max: '9.546e-02'
+  mean: '2.813e-04'
+  min: '-9.485e-02'
+  shape:
+  - 64
+  - 3
+  - 7
+  - 7
+  sum: '2.647e+00'
+network.fc.bias:
+  device: cpu
+  max: '2.099e-02'
+  mean: '3.631e-03'
+  min: '-2.276e-02'
+  shape:
+  - 10
+  sum: '3.631e-02'
+network.fc.weight:
+  device: cpu
+  max: '2.31e-02'
+  mean: '2.87e-04'
+  min: '-2.31e-02'
+  shape:
+  - 10
+  - 2048
+  sum: '5.877e+00'
+network.layer1.0.bn1.bias:
+  device: cpu
+  max: '1.000e-03'
+  mean: '-2.5e-04'
+  min: '-1.000e-03'
+  shape:
+  - 64
+  sum: '-1.6e-02'
+network.layer1.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.0.bn1.running_mean:
+  device: cpu
+  max: '2.719e-01'
+  mean: '-1.677e-02'
+  min: '-2.418e-01'
+  shape:
+  - 64
+  sum: '-1.073e+00'
+network.layer1.0.bn1.running_var:
+  device: cpu
+  max: '1.254e+00'
+  mean: '1.033e+00'
+  min: '9.334e-01'
+  shape:
+  - 64
+  sum: '6.611e+01'
+network.layer1.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.400e+01'
+network.layer1.0.bn2.bias:
+  device: cpu
+  max: '1.000e-03'
+  mean: '3.125e-05'
+  min: '-1.000e-03'
+  shape:
+  - 64
+  sum: '2.000e-03'
+network.layer1.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.0.bn2.running_mean:
+  device: cpu
+  max: '1.243e-01'
+  mean: '-8.254e-03'
+  min: '-9.676e-02'
+  shape:
+  - 64
+  sum: '-5.283e-01'
+network.layer1.0.bn2.running_var:
+  device: cpu
+  max: '1.054e+00'
+  mean: '9.603e-01'
+  min: '9.313e-01'
+  shape:
+  - 64
+  sum: '6.146e+01'
+network.layer1.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.4e+01'
+network.layer1.0.bn3.bias:
+  device: cpu
+  max: '1.000e-03'
+  mean: '-2.344e-05'
+  min: '-1.000e-03'
+  shape:
+  - 256
+  sum: '-6.000e-03'
+network.layer1.0.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.0.bn3.running_mean:
+  device: cpu
+  max: '8.693e-02'
+  mean: '6.465e-04'
+  min: '-7.782e-02'
+  shape:
+  - 256
+  sum: '1.655e-01'
+network.layer1.0.bn3.running_var:
+  device: cpu
+  max: '9.333e-01'
+  mean: '9.173e-01'
+  min: '9.087e-01'
+  shape:
+  - 256
+  sum: '2.348e+02'
+network.layer1.0.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer1.0.conv1.weight:
+  device: cpu
+  max: '7.091e-01'
+  mean: '-3.236e-03'
+  min: '-6.617e-01'
+  shape:
+  - 64
+  - 64
+  - 1
+  - 1
+  sum: '-1.325e+01'
+network.layer1.0.conv2.weight:
+  device: cpu
+  max: '2.499e-01'
+  mean: '-3.63e-04'
+  min: '-2.340e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '-1.338e+01'
+network.layer1.0.conv3.weight:
+  device: cpu
+  max: '3.167e-01'
+  mean: '2.606e-04'
+  min: '-3.587e-01'
+  shape:
+  - 256
+  - 64
+  - 1
+  - 1
+  sum: '4.27e+00'
+network.layer1.0.downsample.0.weight:
+  device: cpu
+  max: '3.360e-01'
+  mean: '3.907e-04'
+  min: '-3.379e-01'
+  shape:
+  - 256
+  - 64
+  - 1
+  - 1
+  sum: '6.402e+00'
+network.layer1.0.downsample.1.bias:
+  device: cpu
+  max: '1.000e-03'
+  mean: '-2.344e-05'
+  min: '-1.000e-03'
+  shape:
+  - 256
+  sum: '-6.000e-03'
+network.layer1.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.0.downsample.1.running_mean:
+  device: cpu
+  max: '1.654e-01'
+  mean: '2.808e-03'
+  min: '-1.828e-01'
+  shape:
+  - 256
+  sum: '7.189e-01'
+network.layer1.0.downsample.1.running_var:
+  device: cpu
+  max: '1.013e+00'
+  mean: '9.321e-01'
+  min: '9.077e-01'
+  shape:
+  - 256
+  sum: '2.386e+02'
+network.layer1.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer1.1.bn1.bias:
+  device: cpu
+  max: '1.000e-03'
+  mean: '4.002e-11'
+  min: '-1.000e-03'
+  shape:
+  - 64
+  sum: '2.561e-09'
+network.layer1.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.1.bn1.running_mean:
+  device: cpu
+  max: '2.900e-01'
+  mean: '2.891e-03'
+  min: '-4.076e-01'
+  shape:
+  - 64
+  sum: '1.850e-01'
+network.layer1.1.bn1.running_var:
+  device: cpu
+  max: '1.77e+00'
+  mean: '1.434e+00'
+  min: '1.164e+00'
+  shape:
+  - 64
+  sum: '9.176e+01'
+network.layer1.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.4e+01'
+network.layer1.1.bn2.bias:
+  device: cpu
+  max: '1.000e-03'
+  mean: '3.125e-05'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '2.000e-03'
+network.layer1.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.1.bn2.running_mean:
+  device: cpu
+  max: '8.965e-02'
+  mean: '-1.15e-03'
+  min: '-1.494e-01'
+  shape:
+  - 64
+  sum: '-7.359e-02'
+network.layer1.1.bn2.running_var:
+  device: cpu
+  max: '1.010e+00'
+  mean: '9.631e-01'
+  min: '9.427e-01'
+  shape:
+  - 64
+  sum: '6.164e+01'
+network.layer1.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.401e+01'
+network.layer1.1.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.563e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-4.000e-03'
+network.layer1.1.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.1.bn3.running_mean:
+  device: cpu
+  max: '6.599e-02'
+  mean: '-2.739e-03'
+  min: '-8.425e-02'
+  shape:
+  - 256
+  sum: '-7.011e-01'
+network.layer1.1.bn3.running_var:
+  device: cpu
+  max: '9.375e-01'
+  mean: '9.178e-01'
+  min: '9.091e-01'
+  shape:
+  - 256
+  sum: '2.349e+02'
+network.layer1.1.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer1.1.conv1.weight:
+  device: cpu
+  max: '7.018e-01'
+  mean: '3.606e-04'
+  min: '-6.553e-01'
+  shape:
+  - 64
+  - 256
+  - 1
+  - 1
+  sum: '5.908e+00'
+network.layer1.1.conv2.weight:
+  device: cpu
+  max: '2.559e-01'
+  mean: '1.564e-05'
+  min: '-2.306e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '5.765e-01'
+network.layer1.1.conv3.weight:
+  device: cpu
+  max: '3.325e-01'
+  mean: '-1.105e-03'
+  min: '-3.437e-01'
+  shape:
+  - 256
+  - 64
+  - 1
+  - 1
+  sum: '-1.810e+01'
+network.layer1.2.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-9.375e-05'
+  min: '-1.000e-03'
+  shape:
+  - 64
+  sum: '-6.000e-03'
+network.layer1.2.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.2.bn1.running_mean:
+  device: cpu
+  max: '5.349e-01'
+  mean: '4.757e-02'
+  min: '-4.288e-01'
+  shape:
+  - 64
+  sum: '3.045e+00'
+network.layer1.2.bn1.running_var:
+  device: cpu
+  max: '2.484e+00'
+  mean: '1.723e+00'
+  min: '1.382e+00'
+  shape:
+  - 64
+  sum: '1.103e+02'
+network.layer1.2.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.400e+01'
+network.layer1.2.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '1.250e-04'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '8.000e-03'
+network.layer1.2.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.2.bn2.running_mean:
+  device: cpu
+  max: '9.456e-02'
+  mean: '4.571e-03'
+  min: '-1.032e-01'
+  shape:
+  - 64
+  sum: '2.926e-01'
+network.layer1.2.bn2.running_var:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.645e-01'
+  min: '9.432e-01'
+  shape:
+  - 64
+  sum: '6.173e+01'
+network.layer1.2.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.4e+01'
+network.layer1.2.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-8.594e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-2.2e-02'
+network.layer1.2.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.2.bn3.running_mean:
+  device: cpu
+  max: '7.621e-02'
+  mean: '-1.462e-03'
+  min: '-8.657e-02'
+  shape:
+  - 256
+  sum: '-3.742e-01'
+network.layer1.2.bn3.running_var:
+  device: cpu
+  max: '9.356e-01'
+  mean: '9.181e-01'
+  min: '9.091e-01'
+  shape:
+  - 256
+  sum: '2.350e+02'
+network.layer1.2.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer1.2.conv1.weight:
+  device: cpu
+  max: '7.088e-01'
+  mean: '2.2e-03'
+  min: '-6.698e-01'
+  shape:
+  - 64
+  - 256
+  - 1
+  - 1
+  sum: '3.604e+01'
+network.layer1.2.conv2.weight:
+  device: cpu
+  max: '2.578e-01'
+  mean: '2.944e-04'
+  min: '-2.371e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '1.085e+01'
+network.layer1.2.conv3.weight:
+  device: cpu
+  max: '3.433e-01'
+  mean: '-5.915e-04'
+  min: '-3.486e-01'
+  shape:
+  - 256
+  - 64
+  - 1
+  - 1
+  sum: '-9.692e+00'
+network.layer2.0.bn1.bias:
+  device: cpu
+  max: '1.000e-03'
+  mean: '-4.648e-10'
+  min: '-1.000e-03'
+  shape:
+  - 128
+  sum: '-5.949e-08'
+network.layer2.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.bn1.running_mean:
+  device: cpu
+  max: '5.129e-01'
+  mean: '7.731e-05'
+  min: '-6.572e-01'
+  shape:
+  - 128
+  sum: '9.896e-03'
+network.layer2.0.bn1.running_var:
+  device: cpu
+  max: '1.985e+00'
+  mean: '1.475e+00'
+  min: '1.245e+00'
+  shape:
+  - 128
+  sum: '1.888e+02'
+network.layer2.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.28e+02'
+network.layer2.0.bn2.bias:
+  device: cpu
+  max: '1.000e-03'
+  mean: '-1.562e-05'
+  min: '-1.000e-03'
+  shape:
+  - 128
+  sum: '-2.e-03'
+network.layer2.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.bn2.running_mean:
+  device: cpu
+  max: '1.467e-01'
+  mean: '9.445e-03'
+  min: '-9.168e-02'
+  shape:
+  - 128
+  sum: '1.209e+00'
+network.layer2.0.bn2.running_var:
+  device: cpu
+  max: '1.006e+00'
+  mean: '9.640e-01'
+  min: '9.46e-01'
+  shape:
+  - 128
+  sum: '1.234e+02'
+network.layer2.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.0.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.562e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-8.e-03'
+network.layer2.0.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.bn3.running_mean:
+  device: cpu
+  max: '7.584e-02'
+  mean: '1.190e-03'
+  min: '-8.802e-02'
+  shape:
+  - 512
+  sum: '6.095e-01'
+network.layer2.0.bn3.running_var:
+  device: cpu
+  max: '9.336e-01'
+  mean: '9.178e-01'
+  min: '9.112e-01'
+  shape:
+  - 512
+  sum: '4.699e+02'
+network.layer2.0.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.120e+02'
+network.layer2.0.conv1.weight:
+  device: cpu
+  max: '5.205e-01'
+  mean: '2.206e-05'
+  min: '-5.177e-01'
+  shape:
+  - 128
+  - 256
+  - 1
+  - 1
+  sum: '7.23e-01'
+network.layer2.0.conv2.weight:
+  device: cpu
+  max: '1.870e-01'
+  mean: '2.507e-04'
+  min: '-1.726e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '3.697e+01'
+network.layer2.0.conv3.weight:
+  device: cpu
+  max: '2.556e-01'
+  mean: '2.431e-04'
+  min: '-2.551e-01'
+  shape:
+  - 512
+  - 128
+  - 1
+  - 1
+  sum: '1.593e+01'
+network.layer2.0.downsample.0.weight:
+  device: cpu
+  max: '3.055e-01'
+  mean: '4.767e-05'
+  min: '-2.490e-01'
+  shape:
+  - 512
+  - 256
+  - 1
+  - 1
+  sum: '6.249e+00'
+network.layer2.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.562e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-8.e-03'
+network.layer2.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.downsample.1.running_mean:
+  device: cpu
+  max: '3.036e-01'
+  mean: '1.091e-03'
+  min: '-2.716e-01'
+  shape:
+  - 512
+  sum: '5.587e-01'
+network.layer2.0.downsample.1.running_var:
+  device: cpu
+  max: '1.185e+00'
+  mean: '1.039e+00'
+  min: '9.839e-01'
+  shape:
+  - 512
+  sum: '5.319e+02'
+network.layer2.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer2.1.bn1.bias:
+  device: cpu
+  max: '1.000e-03'
+  mean: '-7.812e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-1.e-02'
+network.layer2.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.1.bn1.running_mean:
+  device: cpu
+  max: '3.840e-01'
+  mean: '-4.834e-03'
+  min: '-4.565e-01'
+  shape:
+  - 128
+  sum: '-6.187e-01'
+network.layer2.1.bn1.running_var:
+  device: cpu
+  max: '2.201e+00'
+  mean: '1.48e+00'
+  min: '1.31e+00'
+  shape:
+  - 128
+  sum: '1.894e+02'
+network.layer2.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '1.094e-04'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '1.4e-02'
+network.layer2.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.1.bn2.running_mean:
+  device: cpu
+  max: '9.412e-02'
+  mean: '-6.228e-03'
+  min: '-8.402e-02'
+  shape:
+  - 128
+  sum: '-7.971e-01'
+network.layer2.1.bn2.running_var:
+  device: cpu
+  max: '1.016e+00'
+  mean: '9.578e-01'
+  min: '9.402e-01'
+  shape:
+  - 128
+  sum: '1.226e+02'
+network.layer2.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.28e+02'
+network.layer2.1.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-5.078e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-2.600e-02'
+network.layer2.1.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.1.bn3.running_mean:
+  device: cpu
+  max: '7.379e-02'
+  mean: '6.179e-04'
+  min: '-1.084e-01'
+  shape:
+  - 512
+  sum: '3.163e-01'
+network.layer2.1.bn3.running_var:
+  device: cpu
+  max: '9.272e-01'
+  mean: '9.169e-01'
+  min: '9.113e-01'
+  shape:
+  - 512
+  sum: '4.695e+02'
+network.layer2.1.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer2.1.conv1.weight:
+  device: cpu
+  max: '5.645e-01'
+  mean: '-1.538e-04'
+  min: '-5.802e-01'
+  shape:
+  - 128
+  - 512
+  - 1
+  - 1
+  sum: '-1.008e+01'
+network.layer2.1.conv2.weight:
+  device: cpu
+  max: '1.922e-01'
+  mean: '-1.729e-04'
+  min: '-1.838e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '-2.549e+01'
+network.layer2.1.conv3.weight:
+  device: cpu
+  max: '2.637e-01'
+  mean: '1.159e-04'
+  min: '-2.825e-01'
+  shape:
+  - 512
+  - 128
+  - 1
+  - 1
+  sum: '7.597e+00'
+network.layer2.2.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-7.812e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-1.e-02'
+network.layer2.2.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.2.bn1.running_mean:
+  device: cpu
+  max: '5.743e-01'
+  mean: '5.584e-03'
+  min: '-4.834e-01'
+  shape:
+  - 128
+  sum: '7.148e-01'
+network.layer2.2.bn1.running_var:
+  device: cpu
+  max: '2.296e+00'
+  mean: '1.733e+00'
+  min: '1.513e+00'
+  shape:
+  - 128
+  sum: '2.218e+02'
+network.layer2.2.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.28e+02'
+network.layer2.2.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '1.094e-04'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '1.400e-02'
+network.layer2.2.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.2.bn2.running_mean:
+  device: cpu
+  max: '1.015e-01'
+  mean: '-1.101e-03'
+  min: '-1.233e-01'
+  shape:
+  - 128
+  sum: '-1.409e-01'
+network.layer2.2.bn2.running_var:
+  device: cpu
+  max: '9.896e-01'
+  mean: '9.573e-01'
+  min: '9.433e-01'
+  shape:
+  - 128
+  sum: '1.225e+02'
+network.layer2.2.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.28e+02'
+network.layer2.2.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-8.594e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-4.400e-02'
+network.layer2.2.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.2.bn3.running_mean:
+  device: cpu
+  max: '7.668e-02'
+  mean: '4.438e-04'
+  min: '-8.128e-02'
+  shape:
+  - 512
+  sum: '2.272e-01'
+network.layer2.2.bn3.running_var:
+  device: cpu
+  max: '9.288e-01'
+  mean: '9.174e-01'
+  min: '9.105e-01'
+  shape:
+  - 512
+  sum: '4.697e+02'
+network.layer2.2.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.120e+02'
+network.layer2.2.conv1.weight:
+  device: cpu
+  max: '5.362e-01'
+  mean: '1.544e-04'
+  min: '-4.76e-01'
+  shape:
+  - 128
+  - 512
+  - 1
+  - 1
+  sum: '1.012e+01'
+network.layer2.2.conv2.weight:
+  device: cpu
+  max: '1.982e-01'
+  mean: '-3.128e-05'
+  min: '-1.771e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '-4.613e+00'
+network.layer2.2.conv3.weight:
+  device: cpu
+  max: '3.028e-01'
+  mean: '9.162e-05'
+  min: '-2.627e-01'
+  shape:
+  - 512
+  - 128
+  - 1
+  - 1
+  sum: '6.004e+00'
+network.layer2.3.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-2.328e-10'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-2.980e-08'
+network.layer2.3.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.3.bn1.running_mean:
+  device: cpu
+  max: '7.34e-01'
+  mean: '-1.875e-02'
+  min: '-8.28e-01'
+  shape:
+  - 128
+  sum: '-2.400e+00'
+network.layer2.3.bn1.running_var:
+  device: cpu
+  max: '2.899e+00'
+  mean: '2.062e+00'
+  min: '1.665e+00'
+  shape:
+  - 128
+  sum: '2.639e+02'
+network.layer2.3.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.3.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-6.25e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-7.999e-03'
+network.layer2.3.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.3.bn2.running_mean:
+  device: cpu
+  max: '1.007e-01'
+  mean: '2.625e-03'
+  min: '-9.385e-02'
+  shape:
+  - 128
+  sum: '3.36e-01'
+network.layer2.3.bn2.running_var:
+  device: cpu
+  max: '9.905e-01'
+  mean: '9.578e-01'
+  min: '9.425e-01'
+  shape:
+  - 128
+  sum: '1.226e+02'
+network.layer2.3.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.3.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-2.734e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-1.4e-02'
+network.layer2.3.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.3.bn3.running_mean:
+  device: cpu
+  max: '7.844e-02'
+  mean: '-4.253e-05'
+  min: '-7.926e-02'
+  shape:
+  - 512
+  sum: '-2.178e-02'
+network.layer2.3.bn3.running_var:
+  device: cpu
+  max: '9.398e-01'
+  mean: '9.176e-01'
+  min: '9.109e-01'
+  shape:
+  - 512
+  sum: '4.698e+02'
+network.layer2.3.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer2.3.conv1.weight:
+  device: cpu
+  max: '5.324e-01'
+  mean: '-3.441e-04'
+  min: '-5.465e-01'
+  shape:
+  - 128
+  - 512
+  - 1
+  - 1
+  sum: '-2.255e+01'
+network.layer2.3.conv2.weight:
+  device: cpu
+  max: '1.763e-01'
+  mean: '9.73e-05'
+  min: '-1.818e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '1.435e+01'
+network.layer2.3.conv3.weight:
+  device: cpu
+  max: '2.385e-01'
+  mean: '1.15e-06'
+  min: '-2.507e-01'
+  shape:
+  - 512
+  - 128
+  - 1
+  - 1
+  sum: '7.534e-02'
+network.layer3.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.906e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-1.e-02'
+network.layer3.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.bn1.running_mean:
+  device: cpu
+  max: '6.355e-01'
+  mean: '-7.806e-03'
+  min: '-7.964e-01'
+  shape:
+  - 256
+  sum: '-1.998e+00'
+network.layer3.0.bn1.running_var:
+  device: cpu
+  max: '2.25e+00'
+  mean: '1.631e+00'
+  min: '1.408e+00'
+  shape:
+  - 256
+  sum: '4.175e+02'
+network.layer3.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.562e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-4.e-03'
+network.layer3.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.bn2.running_mean:
+  device: cpu
+  max: '9.406e-02'
+  mean: '2.06e-04'
+  min: '-1.084e-01'
+  shape:
+  - 256
+  sum: '5.272e-02'
+network.layer3.0.bn2.running_var:
+  device: cpu
+  max: '9.943e-01'
+  mean: '9.539e-01'
+  min: '9.365e-01'
+  shape:
+  - 256
+  sum: '2.442e+02'
+network.layer3.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.0.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-5.078e-05'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '-5.200e-02'
+network.layer3.0.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.bn3.running_mean:
+  device: cpu
+  max: '7.282e-02'
+  mean: '1.27e-04'
+  min: '-8.503e-02'
+  shape:
+  - 1024
+  sum: '1.3e-01'
+network.layer3.0.bn3.running_var:
+  device: cpu
+  max: '9.345e-01'
+  mean: '9.178e-01'
+  min: '9.107e-01'
+  shape:
+  - 1024
+  sum: '9.398e+02'
+network.layer3.0.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.0.conv1.weight:
+  device: cpu
+  max: '3.657e-01'
+  mean: '-1.321e-04'
+  min: '-3.751e-01'
+  shape:
+  - 256
+  - 512
+  - 1
+  - 1
+  sum: '-1.731e+01'
+network.layer3.0.conv2.weight:
+  device: cpu
+  max: '1.535e-01'
+  mean: '4.192e-05'
+  min: '-1.448e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '2.472e+01'
+network.layer3.0.conv3.weight:
+  device: cpu
+  max: '2.07e-01'
+  mean: '1.832e-05'
+  min: '-2.216e-01'
+  shape:
+  - 1024
+  - 256
+  - 1
+  - 1
+  sum: '4.803e+00'
+network.layer3.0.downsample.0.weight:
+  device: cpu
+  max: '1.978e-01'
+  mean: '3.353e-05'
+  min: '-1.996e-01'
+  shape:
+  - 1024
+  - 512
+  - 1
+  - 1
+  sum: '1.758e+01'
+network.layer3.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-5.078e-05'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '-5.200e-02'
+network.layer3.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.downsample.1.running_mean:
+  device: cpu
+  max: '3.966e-01'
+  mean: '1.818e-03'
+  min: '-4.562e-01'
+  shape:
+  - 1024
+  sum: '1.861e+00'
+network.layer3.0.downsample.1.running_var:
+  device: cpu
+  max: '1.5e+00'
+  mean: '1.068e+00'
+  min: '9.982e-01'
+  shape:
+  - 1024
+  sum: '1.093e+03'
+network.layer3.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '7.813e-06'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '2.000e-03'
+network.layer3.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.1.bn1.running_mean:
+  device: cpu
+  max: '3.986e-01'
+  mean: '1.437e-02'
+  min: '-3.878e-01'
+  shape:
+  - 256
+  sum: '3.68e+00'
+network.layer3.1.bn1.running_var:
+  device: cpu
+  max: '1.851e+00'
+  mean: '1.469e+00'
+  min: '1.303e+00'
+  shape:
+  - 256
+  sum: '3.759e+02'
+network.layer3.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-7.835e-06'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-2.006e-03'
+network.layer3.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.1.bn2.running_mean:
+  device: cpu
+  max: '8.16e-02'
+  mean: '5.012e-04'
+  min: '-7.840e-02'
+  shape:
+  - 256
+  sum: '1.283e-01'
+network.layer3.1.bn2.running_var:
+  device: cpu
+  max: '9.853e-01'
+  mean: '9.372e-01'
+  min: '9.252e-01'
+  shape:
+  - 256
+  sum: '2.399e+02'
+network.layer3.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.1.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '4.102e-05'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '4.2e-02'
+network.layer3.1.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.1.bn3.running_mean:
+  device: cpu
+  max: '8.699e-02'
+  mean: '-4.757e-05'
+  min: '-9.919e-02'
+  shape:
+  - 1024
+  sum: '-4.871e-02'
+network.layer3.1.bn3.running_var:
+  device: cpu
+  max: '9.384e-01'
+  mean: '9.178e-01'
+  min: '9.106e-01'
+  shape:
+  - 1024
+  sum: '9.398e+02'
+network.layer3.1.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.1.conv1.weight:
+  device: cpu
+  max: '3.853e-01'
+  mean: '2.739e-04'
+  min: '-4.e-01'
+  shape:
+  - 256
+  - 1024
+  - 1
+  - 1
+  sum: '7.181e+01'
+network.layer3.1.conv2.weight:
+  device: cpu
+  max: '1.37e-01'
+  mean: '-6.879e-06'
+  min: '-1.296e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '-4.058e+00'
+network.layer3.1.conv3.weight:
+  device: cpu
+  max: '2.062e-01'
+  mean: '-1.376e-05'
+  min: '-1.963e-01'
+  shape:
+  - 1024
+  - 256
+  - 1
+  - 1
+  sum: '-3.606e+00'
+network.layer3.2.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '7.812e-06'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '2.e-03'
+network.layer3.2.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.2.bn1.running_mean:
+  device: cpu
+  max: '6.451e-01'
+  mean: '2.946e-04'
+  min: '-6.535e-01'
+  shape:
+  - 256
+  sum: '7.542e-02'
+network.layer3.2.bn1.running_var:
+  device: cpu
+  max: '2.57e+00'
+  mean: '1.748e+00'
+  min: '1.480e+00'
+  shape:
+  - 256
+  sum: '4.476e+02'
+network.layer3.2.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.2.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-5.468e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-1.4e-02'
+network.layer3.2.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.2.bn2.running_mean:
+  device: cpu
+  max: '9.178e-02'
+  mean: '-7.392e-04'
+  min: '-6.596e-02'
+  shape:
+  - 256
+  sum: '-1.892e-01'
+network.layer3.2.bn2.running_var:
+  device: cpu
+  max: '9.824e-01'
+  mean: '9.371e-01'
+  min: '9.25e-01'
+  shape:
+  - 256
+  sum: '2.399e+02'
+network.layer3.2.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.2.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.954e-05'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '-2.001e-02'
+network.layer3.2.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.2.bn3.running_mean:
+  device: cpu
+  max: '8.711e-02'
+  mean: '8.148e-04'
+  min: '-8.588e-02'
+  shape:
+  - 1024
+  sum: '8.344e-01'
+network.layer3.2.bn3.running_var:
+  device: cpu
+  max: '9.331e-01'
+  mean: '9.173e-01'
+  min: '9.104e-01'
+  shape:
+  - 1024
+  sum: '9.393e+02'
+network.layer3.2.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.2.conv1.weight:
+  device: cpu
+  max: '4.050e-01'
+  mean: '4.085e-06'
+  min: '-4.119e-01'
+  shape:
+  - 256
+  - 1024
+  - 1
+  - 1
+  sum: '1.071e+00'
+network.layer3.2.conv2.weight:
+  device: cpu
+  max: '1.371e-01'
+  mean: '-2.055e-05'
+  min: '-1.515e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '-1.212e+01'
+network.layer3.2.conv3.weight:
+  device: cpu
+  max: '1.974e-01'
+  mean: '8.783e-05'
+  min: '-1.871e-01'
+  shape:
+  - 1024
+  - 256
+  - 1
+  - 1
+  sum: '2.302e+01'
+network.layer3.3.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '1.154e-08'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '2.955e-06'
+network.layer3.3.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.3.bn1.running_mean:
+  device: cpu
+  max: '9.578e-01'
+  mean: '-1.44e-02'
+  min: '-8.003e-01'
+  shape:
+  - 256
+  sum: '-3.685e+00'
+network.layer3.3.bn1.running_var:
+  device: cpu
+  max: '2.748e+00'
+  mean: '2.033e+00'
+  min: '1.701e+00'
+  shape:
+  - 256
+  sum: '5.204e+02'
+network.layer3.3.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.3.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.124e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-7.999e-03'
+network.layer3.3.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.3.bn2.running_mean:
+  device: cpu
+  max: '6.882e-02'
+  mean: '-2.82e-03'
+  min: '-6.876e-02'
+  shape:
+  - 256
+  sum: '-7.218e-01'
+network.layer3.3.bn2.running_var:
+  device: cpu
+  max: '9.893e-01'
+  mean: '9.369e-01'
+  min: '9.213e-01'
+  shape:
+  - 256
+  sum: '2.398e+02'
+network.layer3.3.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.3.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '2.93e-05'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '3.000e-02'
+network.layer3.3.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.3.bn3.running_mean:
+  device: cpu
+  max: '1.070e-01'
+  mean: '-5.055e-04'
+  min: '-8.822e-02'
+  shape:
+  - 1024
+  sum: '-5.177e-01'
+network.layer3.3.bn3.running_var:
+  device: cpu
+  max: '9.348e-01'
+  mean: '9.176e-01'
+  min: '9.107e-01'
+  shape:
+  - 1024
+  sum: '9.396e+02'
+network.layer3.3.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.3.conv1.weight:
+  device: cpu
+  max: '3.84e-01'
+  mean: '-1.425e-04'
+  min: '-4.114e-01'
+  shape:
+  - 256
+  - 1024
+  - 1
+  - 1
+  sum: '-3.734e+01'
+network.layer3.3.conv2.weight:
+  device: cpu
+  max: '1.49e-01'
+  mean: '-4.028e-05'
+  min: '-1.433e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '-2.376e+01'
+network.layer3.3.conv3.weight:
+  device: cpu
+  max: '1.982e-01'
+  mean: '-5.136e-05'
+  min: '-2.077e-01'
+  shape:
+  - 1024
+  - 256
+  - 1
+  - 1
+  sum: '-1.346e+01'
+network.layer3.4.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.906e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-1.e-02'
+network.layer3.4.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.4.bn1.running_mean:
+  device: cpu
+  max: '9.391e-01'
+  mean: '1.164e-02'
+  min: '-8.805e-01'
+  shape:
+  - 256
+  sum: '2.981e+00'
+network.layer3.4.bn1.running_var:
+  device: cpu
+  max: '3.324e+00'
+  mean: '2.335e+00'
+  min: '1.872e+00'
+  shape:
+  - 256
+  sum: '5.978e+02'
+network.layer3.4.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.4.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.125e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-8.000e-03'
+network.layer3.4.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.4.bn2.running_mean:
+  device: cpu
+  max: '7.335e-02'
+  mean: '2.877e-04'
+  min: '-5.897e-02'
+  shape:
+  - 256
+  sum: '7.365e-02'
+network.layer3.4.bn2.running_var:
+  device: cpu
+  max: '9.752e-01'
+  mean: '9.376e-01'
+  min: '9.231e-01'
+  shape:
+  - 256
+  sum: '2.400e+02'
+network.layer3.4.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.4.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '2.539e-05'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '2.599e-02'
+network.layer3.4.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.4.bn3.running_mean:
+  device: cpu
+  max: '8.076e-02'
+  mean: '3.657e-04'
+  min: '-9.05e-02'
+  shape:
+  - 1024
+  sum: '3.745e-01'
+network.layer3.4.bn3.running_var:
+  device: cpu
+  max: '9.331e-01'
+  mean: '9.175e-01'
+  min: '9.104e-01'
+  shape:
+  - 1024
+  sum: '9.395e+02'
+network.layer3.4.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.4.conv1.weight:
+  device: cpu
+  max: '4.366e-01'
+  mean: '1.089e-04'
+  min: '-3.882e-01'
+  shape:
+  - 256
+  - 1024
+  - 1
+  - 1
+  sum: '2.856e+01'
+network.layer3.4.conv2.weight:
+  device: cpu
+  max: '1.440e-01'
+  mean: '-2.725e-05'
+  min: '-1.335e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '-1.607e+01'
+network.layer3.4.conv3.weight:
+  device: cpu
+  max: '1.983e-01'
+  mean: '3.978e-05'
+  min: '-2.036e-01'
+  shape:
+  - 1024
+  - 256
+  - 1
+  - 1
+  sum: '1.043e+01'
+network.layer3.5.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '2.344e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '6.000e-03'
+network.layer3.5.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.5.bn1.running_mean:
+  device: cpu
+  max: '1.069e+00'
+  mean: '4.835e-03'
+  min: '-1.263e+00'
+  shape:
+  - 256
+  sum: '1.238e+00'
+network.layer3.5.bn1.running_var:
+  device: cpu
+  max: '5.264e+00'
+  mean: '2.666e+00'
+  min: '2.039e+00'
+  shape:
+  - 256
+  sum: '6.826e+02'
+network.layer3.5.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.5.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-7.807e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-1.998e-02'
+network.layer3.5.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.5.bn2.running_mean:
+  device: cpu
+  max: '8.408e-02'
+  mean: '-1.709e-04'
+  min: '-8.787e-02'
+  shape:
+  - 256
+  sum: '-4.374e-02'
+network.layer3.5.bn2.running_var:
+  device: cpu
+  max: '1.002e+00'
+  mean: '9.374e-01'
+  min: '9.232e-01'
+  shape:
+  - 256
+  sum: '2.4e+02'
+network.layer3.5.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.5.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '3.711e-05'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '3.800e-02'
+network.layer3.5.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.5.bn3.running_mean:
+  device: cpu
+  max: '9.598e-02'
+  mean: '-1.156e-03'
+  min: '-7.857e-02'
+  shape:
+  - 1024
+  sum: '-1.184e+00'
+network.layer3.5.bn3.running_var:
+  device: cpu
+  max: '9.395e-01'
+  mean: '9.183e-01'
+  min: '9.105e-01'
+  shape:
+  - 1024
+  sum: '9.404e+02'
+network.layer3.5.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.5.conv1.weight:
+  device: cpu
+  max: '4.085e-01'
+  mean: '6.668e-05'
+  min: '-3.796e-01'
+  shape:
+  - 256
+  - 1024
+  - 1
+  - 1
+  sum: '1.748e+01'
+network.layer3.5.conv2.weight:
+  device: cpu
+  max: '1.351e-01'
+  mean: '-1.128e-05'
+  min: '-1.371e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '-6.655e+00'
+network.layer3.5.conv3.weight:
+  device: cpu
+  max: '1.978e-01'
+  mean: '-1.088e-04'
+  min: '-2.030e-01'
+  shape:
+  - 1024
+  - 256
+  - 1
+  - 1
+  sum: '-2.853e+01'
+network.layer4.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.328e-04'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-6.800e-02'
+network.layer4.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.bn1.running_mean:
+  device: cpu
+  max: '9.640e-01'
+  mean: '8.711e-03'
+  min: '-1.012e+00'
+  shape:
+  - 512
+  sum: '4.46e+00'
+network.layer4.0.bn1.running_var:
+  device: cpu
+  max: '3.267e+00'
+  mean: '1.935e+00'
+  min: '1.514e+00'
+  shape:
+  - 512
+  sum: '9.909e+02'
+network.layer4.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.563e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-8.001e-03'
+network.layer4.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.bn2.running_mean:
+  device: cpu
+  max: '1.138e-01'
+  mean: '2.026e-03'
+  min: '-1.109e-01'
+  shape:
+  - 512
+  sum: '1.037e+00'
+network.layer4.0.bn2.running_var:
+  device: cpu
+  max: '9.866e-01'
+  mean: '9.303e-01'
+  min: '9.169e-01'
+  shape:
+  - 512
+  sum: '4.763e+02'
+network.layer4.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.0.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-2.930e-05'
+  min: '-1.e-03'
+  shape:
+  - 2048
+  sum: '-6.002e-02'
+network.layer4.0.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.bn3.running_mean:
+  device: cpu
+  max: '9.188e-02'
+  mean: '-3.112e-06'
+  min: '-8.592e-02'
+  shape:
+  - 2048
+  sum: '-6.373e-03'
+network.layer4.0.bn3.running_var:
+  device: cpu
+  max: '9.583e-01'
+  mean: '9.183e-01'
+  min: '9.081e-01'
+  shape:
+  - 2048
+  sum: '1.881e+03'
+network.layer4.0.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 2048
+  sum: '2.048e+03'
+network.layer4.0.conv1.weight:
+  device: cpu
+  max: '2.962e-01'
+  mean: '6.945e-05'
+  min: '-2.893e-01'
+  shape:
+  - 512
+  - 1024
+  - 1
+  - 1
+  sum: '3.641e+01'
+network.layer4.0.conv2.weight:
+  device: cpu
+  max: '1.009e-01'
+  mean: '1.558e-05'
+  min: '-1.102e-01'
+  shape:
+  - 512
+  - 512
+  - 3
+  - 3
+  sum: '3.675e+01'
+network.layer4.0.conv3.weight:
+  device: cpu
+  max: '1.524e-01'
+  mean: '-4.276e-06'
+  min: '-1.663e-01'
+  shape:
+  - 2048
+  - 512
+  - 1
+  - 1
+  sum: '-4.484e+00'
+network.layer4.0.downsample.0.weight:
+  device: cpu
+  max: '1.485e-01'
+  mean: '-7.490e-06'
+  min: '-1.482e-01'
+  shape:
+  - 2048
+  - 1024
+  - 1
+  - 1
+  sum: '-1.571e+01'
+network.layer4.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-2.930e-05'
+  min: '-1.e-03'
+  shape:
+  - 2048
+  sum: '-6.002e-02'
+network.layer4.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.downsample.1.running_mean:
+  device: cpu
+  max: '5.176e-01'
+  mean: '-2.323e-03'
+  min: '-5.419e-01'
+  shape:
+  - 2048
+  sum: '-4.758e+00'
+network.layer4.0.downsample.1.running_var:
+  device: cpu
+  max: '1.705e+00'
+  mean: '1.114e+00'
+  min: '1.008e+00'
+  shape:
+  - 2048
+  sum: '2.282e+03'
+network.layer4.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 2048
+  sum: '2.048e+03'
+network.layer4.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '3.516e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '1.8e-02'
+network.layer4.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.1.bn1.running_mean:
+  device: cpu
+  max: '4.858e-01'
+  mean: '6.861e-03'
+  min: '-4.448e-01'
+  shape:
+  - 512
+  sum: '3.513e+00'
+network.layer4.1.bn1.running_var:
+  device: cpu
+  max: '2.39e+00'
+  mean: '1.468e+00'
+  min: '1.217e+00'
+  shape:
+  - 512
+  sum: '7.516e+02'
+network.layer4.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-4.297e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-2.2e-02'
+network.layer4.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.1.bn2.running_mean:
+  device: cpu
+  max: '6.595e-02'
+  mean: '4.983e-04'
+  min: '-5.374e-02'
+  shape:
+  - 512
+  sum: '2.551e-01'
+network.layer4.1.bn2.running_var:
+  device: cpu
+  max: '9.224e-01'
+  mean: '9.080e-01'
+  min: '9.043e-01'
+  shape:
+  - 512
+  sum: '4.649e+02'
+network.layer4.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.1.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-7.518e-05'
+  min: '-1.e-03'
+  shape:
+  - 2048
+  sum: '-1.54e-01'
+network.layer4.1.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.1.bn3.running_mean:
+  device: cpu
+  max: '9.128e-02'
+  mean: '-9.541e-05'
+  min: '-9.597e-02'
+  shape:
+  - 2048
+  sum: '-1.954e-01'
+network.layer4.1.bn3.running_var:
+  device: cpu
+  max: '9.498e-01'
+  mean: '9.179e-01'
+  min: '9.082e-01'
+  shape:
+  - 2048
+  sum: '1.88e+03'
+network.layer4.1.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 2048
+  sum: '2.048e+03'
+network.layer4.1.conv1.weight:
+  device: cpu
+  max: '3.275e-01'
+  mean: '6.301e-05'
+  min: '-3.023e-01'
+  shape:
+  - 512
+  - 2048
+  - 1
+  - 1
+  sum: '6.607e+01'
+network.layer4.1.conv2.weight:
+  device: cpu
+  max: '1.114e-01'
+  mean: '2.580e-05'
+  min: '-1.031e-01'
+  shape:
+  - 512
+  - 512
+  - 3
+  - 3
+  sum: '6.088e+01'
+network.layer4.1.conv3.weight:
+  device: cpu
+  max: '1.493e-01'
+  mean: '-1.013e-05'
+  min: '-1.565e-01'
+  shape:
+  - 2048
+  - 512
+  - 1
+  - 1
+  sum: '-1.062e+01'
+network.layer4.2.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-2.734e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-1.4e-02'
+network.layer4.2.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.2.bn1.running_mean:
+  device: cpu
+  max: '5.742e-01'
+  mean: '-1.955e-02'
+  min: '-6.579e-01'
+  shape:
+  - 512
+  sum: '-1.001e+01'
+network.layer4.2.bn1.running_var:
+  device: cpu
+  max: '3.344e+00'
+  mean: '1.769e+00'
+  min: '1.361e+00'
+  shape:
+  - 512
+  sum: '9.056e+02'
+network.layer4.2.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.120e+02'
+network.layer4.2.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '8.203e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '4.2e-02'
+network.layer4.2.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.2.bn2.running_mean:
+  device: cpu
+  max: '5.707e-02'
+  mean: '-1.127e-03'
+  min: '-6.645e-02'
+  shape:
+  - 512
+  sum: '-5.773e-01'
+network.layer4.2.bn2.running_var:
+  device: cpu
+  max: '9.171e-01'
+  mean: '9.08e-01'
+  min: '9.049e-01'
+  shape:
+  - 512
+  sum: '4.649e+02'
+network.layer4.2.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.2.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-7.028e-05'
+  min: '-1.e-03'
+  shape:
+  - 2048
+  sum: '-1.439e-01'
+network.layer4.2.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.2.bn3.running_mean:
+  device: cpu
+  max: '9.983e-02'
+  mean: '3.687e-04'
+  min: '-8.547e-02'
+  shape:
+  - 2048
+  sum: '7.552e-01'
+network.layer4.2.bn3.running_var:
+  device: cpu
+  max: '9.463e-01'
+  mean: '9.177e-01'
+  min: '9.092e-01'
+  shape:
+  - 2048
+  sum: '1.879e+03'
+network.layer4.2.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 2048
+  sum: '2.048e+03'
+network.layer4.2.conv1.weight:
+  device: cpu
+  max: '2.950e-01'
+  mean: '-1.293e-04'
+  min: '-3.378e-01'
+  shape:
+  - 512
+  - 2048
+  - 1
+  - 1
+  sum: '-1.356e+02'
+network.layer4.2.conv2.weight:
+  device: cpu
+  max: '9.885e-02'
+  mean: '-6.983e-06'
+  min: '-9.988e-02'
+  shape:
+  - 512
+  - 512
+  - 3
+  - 3
+  sum: '-1.647e+01'
+network.layer4.2.conv3.weight:
+  device: cpu
+  max: '1.44e-01'
+  mean: '1.037e-05'
+  min: '-1.568e-01'
+  shape:
+  - 2048
+  - 512
+  - 1
+  - 1
+  sum: '1.088e+01'
diff --git a/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/resnet50_imagenet_image_classifier.yaml b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/resnet50_imagenet_image_classifier.yaml
new file mode 100644
index 00000000..94d3cca9
--- /dev/null
+++ b/.regression_files/project/algorithms/image_classifier_test/test_initialization_is_reproducible/resnet50_imagenet_image_classifier.yaml
@@ -0,0 +1,2667 @@
+network.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '3.125e-05'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '2.e-03'
+network.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.bn1.running_mean:
+  device: cpu
+  max: '3.664e-03'
+  mean: '-2.229e-04'
+  min: '-5.209e-03'
+  shape:
+  - 64
+  sum: '-1.426e-02'
+network.bn1.running_var:
+  device: cpu
+  max: '9.898e-01'
+  mean: '9.132e-01'
+  min: '9.017e-01'
+  shape:
+  - 64
+  sum: '5.845e+01'
+network.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.4e+01'
+network.conv1.weight:
+  device: cpu
+  max: '1.029e-01'
+  mean: '2.141e-04'
+  min: '-8.232e-02'
+  shape:
+  - 64
+  - 3
+  - 7
+  - 7
+  sum: '2.014e+00'
+network.fc.bias:
+  device: cpu
+  max: '2.278e-02'
+  mean: '-4.274e-04'
+  min: '-2.306e-02'
+  shape:
+  - 1000
+  sum: '-4.274e-01'
+network.fc.weight:
+  device: cpu
+  max: '2.31e-02'
+  mean: '-8.699e-04'
+  min: '-2.31e-02'
+  shape:
+  - 1000
+  - 2048
+  sum: '-1.782e+03'
+network.layer1.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-9.375e-05'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '-6.e-03'
+network.layer1.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.0.bn1.running_mean:
+  device: cpu
+  max: '2.051e-01'
+  mean: '6.237e-03'
+  min: '-2.132e-01'
+  shape:
+  - 64
+  sum: '3.992e-01'
+network.layer1.0.bn1.running_var:
+  device: cpu
+  max: '1.229e+00'
+  mean: '1.003e+00'
+  min: '9.199e-01'
+  shape:
+  - 64
+  sum: '6.417e+01'
+network.layer1.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.400e+01'
+network.layer1.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.562e-04'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '-1.e-02'
+network.layer1.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.0.bn2.running_mean:
+  device: cpu
+  max: '1.287e-01'
+  mean: '2.734e-03'
+  min: '-8.406e-02'
+  shape:
+  - 64
+  sum: '1.75e-01'
+network.layer1.0.bn2.running_var:
+  device: cpu
+  max: '1.026e+00'
+  mean: '9.658e-01'
+  min: '9.326e-01'
+  shape:
+  - 64
+  sum: '6.181e+01'
+network.layer1.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.4e+01'
+network.layer1.0.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '2.344e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '6.e-03'
+network.layer1.0.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.0.bn3.running_mean:
+  device: cpu
+  max: '6.554e-02'
+  mean: '9.828e-04'
+  min: '-7.278e-02'
+  shape:
+  - 256
+  sum: '2.516e-01'
+network.layer1.0.bn3.running_var:
+  device: cpu
+  max: '9.477e-01'
+  mean: '9.178e-01'
+  min: '9.071e-01'
+  shape:
+  - 256
+  sum: '2.35e+02'
+network.layer1.0.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer1.0.conv1.weight:
+  device: cpu
+  max: '6.519e-01'
+  mean: '1.460e-03'
+  min: '-6.017e-01'
+  shape:
+  - 64
+  - 64
+  - 1
+  - 1
+  sum: '5.981e+00'
+network.layer1.0.conv2.weight:
+  device: cpu
+  max: '2.369e-01'
+  mean: '1.337e-04'
+  min: '-2.5e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '4.929e+00'
+network.layer1.0.conv3.weight:
+  device: cpu
+  max: '3.842e-01'
+  mean: '3.607e-04'
+  min: '-3.468e-01'
+  shape:
+  - 256
+  - 64
+  - 1
+  - 1
+  sum: '5.910e+00'
+network.layer1.0.downsample.0.weight:
+  device: cpu
+  max: '3.433e-01'
+  mean: '-6.289e-04'
+  min: '-3.466e-01'
+  shape:
+  - 256
+  - 64
+  - 1
+  - 1
+  sum: '-1.030e+01'
+network.layer1.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '2.344e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '6.e-03'
+network.layer1.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.0.downsample.1.running_mean:
+  device: cpu
+  max: '1.389e-01'
+  mean: '-2.514e-03'
+  min: '-1.441e-01'
+  shape:
+  - 256
+  sum: '-6.435e-01'
+network.layer1.0.downsample.1.running_var:
+  device: cpu
+  max: '1.002e+00'
+  mean: '9.280e-01'
+  min: '9.054e-01'
+  shape:
+  - 256
+  sum: '2.376e+02'
+network.layer1.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer1.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-5.821e-11'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '-3.725e-09'
+network.layer1.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.1.bn1.running_mean:
+  device: cpu
+  max: '3.417e-01'
+  mean: '1.193e-02'
+  min: '-4.535e-01'
+  shape:
+  - 64
+  sum: '7.637e-01'
+network.layer1.1.bn1.running_var:
+  device: cpu
+  max: '2.906e+00'
+  mean: '1.516e+00'
+  min: '1.208e+00'
+  shape:
+  - 64
+  sum: '9.701e+01'
+network.layer1.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.400e+01'
+network.layer1.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-9.375e-05'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '-6.000e-03'
+network.layer1.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.1.bn2.running_mean:
+  device: cpu
+  max: '1.189e-01'
+  mean: '7.488e-03'
+  min: '-1.011e-01'
+  shape:
+  - 64
+  sum: '4.792e-01'
+network.layer1.1.bn2.running_var:
+  device: cpu
+  max: '1.021e+00'
+  mean: '9.704e-01'
+  min: '9.466e-01'
+  shape:
+  - 64
+  sum: '6.211e+01'
+network.layer1.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.401e+01'
+network.layer1.1.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '1.016e-04'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '2.6e-02'
+network.layer1.1.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.1.bn3.running_mean:
+  device: cpu
+  max: '7.225e-02'
+  mean: '1.518e-04'
+  min: '-8.057e-02'
+  shape:
+  - 256
+  sum: '3.886e-02'
+network.layer1.1.bn3.running_var:
+  device: cpu
+  max: '9.508e-01'
+  mean: '9.189e-01'
+  min: '9.084e-01'
+  shape:
+  - 256
+  sum: '2.352e+02'
+network.layer1.1.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer1.1.conv1.weight:
+  device: cpu
+  max: '7.357e-01'
+  mean: '1.008e-03'
+  min: '-6.653e-01'
+  shape:
+  - 64
+  - 256
+  - 1
+  - 1
+  sum: '1.651e+01'
+network.layer1.1.conv2.weight:
+  device: cpu
+  max: '2.624e-01'
+  mean: '3.366e-04'
+  min: '-2.227e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '1.241e+01'
+network.layer1.1.conv3.weight:
+  device: cpu
+  max: '3.081e-01'
+  mean: '5.049e-05'
+  min: '-3.567e-01'
+  shape:
+  - 256
+  - 64
+  - 1
+  - 1
+  sum: '8.272e-01'
+network.layer1.2.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-6.250e-05'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '-4.000e-03'
+network.layer1.2.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.2.bn1.running_mean:
+  device: cpu
+  max: '4.702e-01'
+  mean: '-2.824e-02'
+  min: '-4.349e-01'
+  shape:
+  - 64
+  sum: '-1.807e+00'
+network.layer1.2.bn1.running_var:
+  device: cpu
+  max: '2.793e+00'
+  mean: '1.734e+00'
+  min: '1.393e+00'
+  shape:
+  - 64
+  sum: '1.110e+02'
+network.layer1.2.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.400e+01'
+network.layer1.2.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-6.25e-05'
+  min: '-1.e-03'
+  shape:
+  - 64
+  sum: '-4.e-03'
+network.layer1.2.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.2.bn2.running_mean:
+  device: cpu
+  max: '1.115e-01'
+  mean: '-5.358e-04'
+  min: '-1.317e-01'
+  shape:
+  - 64
+  sum: '-3.429e-02'
+network.layer1.2.bn2.running_var:
+  device: cpu
+  max: '1.025e+00'
+  mean: '9.687e-01'
+  min: '9.444e-01'
+  shape:
+  - 64
+  sum: '6.2e+01'
+network.layer1.2.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 64
+  sum: '6.4e+01'
+network.layer1.2.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-2.344e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-6.e-03'
+network.layer1.2.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer1.2.bn3.running_mean:
+  device: cpu
+  max: '5.800e-02'
+  mean: '1.288e-03'
+  min: '-8.365e-02'
+  shape:
+  - 256
+  sum: '3.297e-01'
+network.layer1.2.bn3.running_var:
+  device: cpu
+  max: '9.43e-01'
+  mean: '9.178e-01'
+  min: '9.073e-01'
+  shape:
+  - 256
+  sum: '2.35e+02'
+network.layer1.2.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer1.2.conv1.weight:
+  device: cpu
+  max: '6.514e-01'
+  mean: '-1.424e-03'
+  min: '-7.000e-01'
+  shape:
+  - 64
+  - 256
+  - 1
+  - 1
+  sum: '-2.332e+01'
+network.layer1.2.conv2.weight:
+  device: cpu
+  max: '2.676e-01'
+  mean: '-6.505e-05'
+  min: '-2.337e-01'
+  shape:
+  - 64
+  - 64
+  - 3
+  - 3
+  sum: '-2.398e+00'
+network.layer1.2.conv3.weight:
+  device: cpu
+  max: '3.398e-01'
+  mean: '5.418e-04'
+  min: '-3.081e-01'
+  shape:
+  - 256
+  - 64
+  - 1
+  - 1
+  sum: '8.877e+00'
+network.layer2.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-9.375e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-1.200e-02'
+network.layer2.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.bn1.running_mean:
+  device: cpu
+  max: '4.468e-01'
+  mean: '-1.316e-02'
+  min: '-6.135e-01'
+  shape:
+  - 128
+  sum: '-1.685e+00'
+network.layer2.0.bn1.running_var:
+  device: cpu
+  max: '3.099e+00'
+  mean: '1.468e+00'
+  min: '1.189e+00'
+  shape:
+  - 128
+  sum: '1.879e+02'
+network.layer2.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.28e+02'
+network.layer2.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-4.687e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-6.e-03'
+network.layer2.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.bn2.running_mean:
+  device: cpu
+  max: '1.029e-01'
+  mean: '-4.892e-03'
+  min: '-1.682e-01'
+  shape:
+  - 128
+  sum: '-6.261e-01'
+network.layer2.0.bn2.running_var:
+  device: cpu
+  max: '1.027e+00'
+  mean: '9.690e-01'
+  min: '9.48e-01'
+  shape:
+  - 128
+  sum: '1.240e+02'
+network.layer2.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.28e+02'
+network.layer2.0.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '3.911e-06'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '2.003e-03'
+network.layer2.0.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.bn3.running_mean:
+  device: cpu
+  max: '7.612e-02'
+  mean: '-8.702e-04'
+  min: '-9.060e-02'
+  shape:
+  - 512
+  sum: '-4.455e-01'
+network.layer2.0.bn3.running_var:
+  device: cpu
+  max: '9.348e-01'
+  mean: '9.179e-01'
+  min: '9.101e-01'
+  shape:
+  - 512
+  sum: '4.7e+02'
+network.layer2.0.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.120e+02'
+network.layer2.0.conv1.weight:
+  device: cpu
+  max: '5.166e-01'
+  mean: '-5.482e-04'
+  min: '-5.009e-01'
+  shape:
+  - 128
+  - 256
+  - 1
+  - 1
+  sum: '-1.796e+01'
+network.layer2.0.conv2.weight:
+  device: cpu
+  max: '1.818e-01'
+  mean: '-1.152e-04'
+  min: '-1.897e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '-1.699e+01'
+network.layer2.0.conv3.weight:
+  device: cpu
+  max: '2.885e-01'
+  mean: '-1.687e-04'
+  min: '-2.583e-01'
+  shape:
+  - 512
+  - 128
+  - 1
+  - 1
+  sum: '-1.106e+01'
+network.layer2.0.downsample.0.weight:
+  device: cpu
+  max: '3.028e-01'
+  mean: '-5.015e-05'
+  min: '-2.687e-01'
+  shape:
+  - 512
+  - 256
+  - 1
+  - 1
+  sum: '-6.573e+00'
+network.layer2.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '3.911e-06'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '2.003e-03'
+network.layer2.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.0.downsample.1.running_mean:
+  device: cpu
+  max: '2.881e-01'
+  mean: '-1.386e-03'
+  min: '-3.036e-01'
+  shape:
+  - 512
+  sum: '-7.095e-01'
+network.layer2.0.downsample.1.running_var:
+  device: cpu
+  max: '1.394e+00'
+  mean: '1.047e+00'
+  min: '9.820e-01'
+  shape:
+  - 512
+  sum: '5.359e+02'
+network.layer2.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer2.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.125e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-4.e-03'
+network.layer2.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.1.bn1.running_mean:
+  device: cpu
+  max: '3.813e-01'
+  mean: '-1.154e-02'
+  min: '-4.204e-01'
+  shape:
+  - 128
+  sum: '-1.477e+00'
+network.layer2.1.bn1.running_var:
+  device: cpu
+  max: '2.046e+00'
+  mean: '1.483e+00'
+  min: '1.267e+00'
+  shape:
+  - 128
+  sum: '1.899e+02'
+network.layer2.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-4.687e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-6.e-03'
+network.layer2.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.1.bn2.running_mean:
+  device: cpu
+  max: '1.385e-01'
+  mean: '3.322e-03'
+  min: '-1.15e-01'
+  shape:
+  - 128
+  sum: '4.252e-01'
+network.layer2.1.bn2.running_var:
+  device: cpu
+  max: '1.012e+00'
+  mean: '9.675e-01'
+  min: '9.516e-01'
+  shape:
+  - 128
+  sum: '1.238e+02'
+network.layer2.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.1.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.515e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-1.8e-02'
+network.layer2.1.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.1.bn3.running_mean:
+  device: cpu
+  max: '7.72e-02'
+  mean: '-5.872e-05'
+  min: '-7.637e-02'
+  shape:
+  - 512
+  sum: '-3.006e-02'
+network.layer2.1.bn3.running_var:
+  device: cpu
+  max: '9.485e-01'
+  mean: '9.181e-01'
+  min: '9.105e-01'
+  shape:
+  - 512
+  sum: '4.700e+02'
+network.layer2.1.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer2.1.conv1.weight:
+  device: cpu
+  max: '5.324e-01'
+  mean: '-3.391e-04'
+  min: '-5.465e-01'
+  shape:
+  - 128
+  - 512
+  - 1
+  - 1
+  sum: '-2.222e+01'
+network.layer2.1.conv2.weight:
+  device: cpu
+  max: '1.764e-01'
+  mean: '7.592e-05'
+  min: '-1.798e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '1.12e+01'
+network.layer2.1.conv3.weight:
+  device: cpu
+  max: '2.392e-01'
+  mean: '-9.593e-06'
+  min: '-2.507e-01'
+  shape:
+  - 512
+  - 128
+  - 1
+  - 1
+  sum: '-6.287e-01'
+network.layer2.2.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '1.250e-04'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '1.600e-02'
+network.layer2.2.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.2.bn1.running_mean:
+  device: cpu
+  max: '5.174e-01'
+  mean: '-1.209e-02'
+  min: '-6.209e-01'
+  shape:
+  - 128
+  sum: '-1.547e+00'
+network.layer2.2.bn1.running_var:
+  device: cpu
+  max: '2.799e+00'
+  mean: '1.757e+00'
+  min: '1.492e+00'
+  shape:
+  - 128
+  sum: '2.249e+02'
+network.layer2.2.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.28e+02'
+network.layer2.2.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.125e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-4.000e-03'
+network.layer2.2.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.2.bn2.running_mean:
+  device: cpu
+  max: '2.016e-01'
+  mean: '-5.056e-04'
+  min: '-1.190e-01'
+  shape:
+  - 128
+  sum: '-6.471e-02'
+network.layer2.2.bn2.running_var:
+  device: cpu
+  max: '1.047e+00'
+  mean: '9.72e-01'
+  min: '9.52e-01'
+  shape:
+  - 128
+  sum: '1.244e+02'
+network.layer2.2.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.2.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '7.798e-06'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '3.993e-03'
+network.layer2.2.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.2.bn3.running_mean:
+  device: cpu
+  max: '8.048e-02'
+  mean: '1.639e-04'
+  min: '-7.626e-02'
+  shape:
+  - 512
+  sum: '8.392e-02'
+network.layer2.2.bn3.running_var:
+  device: cpu
+  max: '9.442e-01'
+  mean: '9.177e-01'
+  min: '9.099e-01'
+  shape:
+  - 512
+  sum: '4.699e+02'
+network.layer2.2.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.120e+02'
+network.layer2.2.conv1.weight:
+  device: cpu
+  max: '4.961e-01'
+  mean: '-3.071e-04'
+  min: '-5.301e-01'
+  shape:
+  - 128
+  - 512
+  - 1
+  - 1
+  sum: '-2.013e+01'
+network.layer2.2.conv2.weight:
+  device: cpu
+  max: '2.097e-01'
+  mean: '-5.323e-06'
+  min: '-1.769e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '-7.85e-01'
+network.layer2.2.conv3.weight:
+  device: cpu
+  max: '3.226e-01'
+  mean: '3.012e-05'
+  min: '-3.016e-01'
+  shape:
+  - 512
+  - 128
+  - 1
+  - 1
+  sum: '1.974e+00'
+network.layer2.3.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.562e-04'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-2.e-02'
+network.layer2.3.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.3.bn1.running_mean:
+  device: cpu
+  max: '7.481e-01'
+  mean: '1.749e-03'
+  min: '-6.104e-01'
+  shape:
+  - 128
+  sum: '2.238e-01'
+network.layer2.3.bn1.running_var:
+  device: cpu
+  max: '3.514e+00'
+  mean: '2.075e+00'
+  min: '1.738e+00'
+  shape:
+  - 128
+  sum: '2.656e+02'
+network.layer2.3.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.280e+02'
+network.layer2.3.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.124e-05'
+  min: '-1.e-03'
+  shape:
+  - 128
+  sum: '-3.998e-03'
+network.layer2.3.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.3.bn2.running_mean:
+  device: cpu
+  max: '1.383e-01'
+  mean: '2.598e-03'
+  min: '-1.551e-01'
+  shape:
+  - 128
+  sum: '3.325e-01'
+network.layer2.3.bn2.running_var:
+  device: cpu
+  max: '1.006e+00'
+  mean: '9.688e-01'
+  min: '9.529e-01'
+  shape:
+  - 128
+  sum: '1.240e+02'
+network.layer2.3.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 128
+  sum: '1.28e+02'
+network.layer2.3.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '2.055e-09'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '1.052e-06'
+network.layer2.3.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer2.3.bn3.running_mean:
+  device: cpu
+  max: '6.402e-02'
+  mean: '-1.315e-03'
+  min: '-6.971e-02'
+  shape:
+  - 512
+  sum: '-6.735e-01'
+network.layer2.3.bn3.running_var:
+  device: cpu
+  max: '9.427e-01'
+  mean: '9.184e-01'
+  min: '9.100e-01'
+  shape:
+  - 512
+  sum: '4.702e+02'
+network.layer2.3.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer2.3.conv1.weight:
+  device: cpu
+  max: '5.327e-01'
+  mean: '1.254e-04'
+  min: '-5.187e-01'
+  shape:
+  - 128
+  - 512
+  - 1
+  - 1
+  sum: '8.221e+00'
+network.layer2.3.conv2.weight:
+  device: cpu
+  max: '1.864e-01'
+  mean: '7.521e-05'
+  min: '-1.845e-01'
+  shape:
+  - 128
+  - 128
+  - 3
+  - 3
+  sum: '1.109e+01'
+network.layer2.3.conv3.weight:
+  device: cpu
+  max: '2.569e-01'
+  mean: '-2.714e-04'
+  min: '-2.538e-01'
+  shape:
+  - 512
+  - 128
+  - 1
+  - 1
+  sum: '-1.779e+01'
+network.layer3.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '7.03e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '1.8e-02'
+network.layer3.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.bn1.running_mean:
+  device: cpu
+  max: '5.803e-01'
+  mean: '2.064e-02'
+  min: '-5.798e-01'
+  shape:
+  - 256
+  sum: '5.284e+00'
+network.layer3.0.bn1.running_var:
+  device: cpu
+  max: '2.985e+00'
+  mean: '1.648e+00'
+  min: '1.38e+00'
+  shape:
+  - 256
+  sum: '4.22e+02'
+network.layer3.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '5.469e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '1.400e-02'
+network.layer3.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.bn2.running_mean:
+  device: cpu
+  max: '1.31e-01'
+  mean: '-3.847e-04'
+  min: '-1.449e-01'
+  shape:
+  - 256
+  sum: '-9.848e-02'
+network.layer3.0.bn2.running_var:
+  device: cpu
+  max: '1.081e+00'
+  mean: '9.722e-01'
+  min: '9.508e-01'
+  shape:
+  - 256
+  sum: '2.489e+02'
+network.layer3.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.0.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-9.772e-06'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '-1.001e-02'
+network.layer3.0.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.bn3.running_mean:
+  device: cpu
+  max: '8.373e-02'
+  mean: '-4.925e-05'
+  min: '-9.546e-02'
+  shape:
+  - 1024
+  sum: '-5.043e-02'
+network.layer3.0.bn3.running_var:
+  device: cpu
+  max: '9.409e-01'
+  mean: '9.181e-01'
+  min: '9.115e-01'
+  shape:
+  - 1024
+  sum: '9.401e+02'
+network.layer3.0.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.0.conv1.weight:
+  device: cpu
+  max: '3.851e-01'
+  mean: '3.77e-04'
+  min: '-4.e-01'
+  shape:
+  - 256
+  - 512
+  - 1
+  - 1
+  sum: '4.941e+01'
+network.layer3.0.conv2.weight:
+  device: cpu
+  max: '1.39e-01'
+  mean: '-2.224e-06'
+  min: '-1.304e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '-1.312e+00'
+network.layer3.0.conv3.weight:
+  device: cpu
+  max: '2.042e-01'
+  mean: '-9.624e-06'
+  min: '-1.963e-01'
+  shape:
+  - 1024
+  - 256
+  - 1
+  - 1
+  sum: '-2.523e+00'
+network.layer3.0.downsample.0.weight:
+  device: cpu
+  max: '2.030e-01'
+  mean: '4.344e-06'
+  min: '-2.247e-01'
+  shape:
+  - 1024
+  - 512
+  - 1
+  - 1
+  sum: '2.278e+00'
+network.layer3.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-9.772e-06'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '-1.001e-02'
+network.layer3.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.0.downsample.1.running_mean:
+  device: cpu
+  max: '4.055e-01'
+  mean: '8.438e-04'
+  min: '-4.094e-01'
+  shape:
+  - 1024
+  sum: '8.640e-01'
+network.layer3.0.downsample.1.running_var:
+  device: cpu
+  max: '1.455e+00'
+  mean: '1.087e+00'
+  min: '1.011e+00'
+  shape:
+  - 1024
+  sum: '1.114e+03'
+network.layer3.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-8.594e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-2.200e-02'
+network.layer3.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.1.bn1.running_mean:
+  device: cpu
+  max: '5.011e-01'
+  mean: '9.704e-04'
+  min: '-4.797e-01'
+  shape:
+  - 256
+  sum: '2.484e-01'
+network.layer3.1.bn1.running_var:
+  device: cpu
+  max: '2.568e+00'
+  mean: '1.479e+00'
+  min: '1.312e+00'
+  shape:
+  - 256
+  sum: '3.786e+02'
+network.layer3.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.126e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-8.002e-03'
+network.layer3.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.1.bn2.running_mean:
+  device: cpu
+  max: '1.302e-01'
+  mean: '7.955e-04'
+  min: '-1.34e-01'
+  shape:
+  - 256
+  sum: '2.036e-01'
+network.layer3.1.bn2.running_var:
+  device: cpu
+  max: '1.025e+00'
+  mean: '9.671e-01'
+  min: '9.554e-01'
+  shape:
+  - 256
+  sum: '2.476e+02'
+network.layer3.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.1.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.129e-05'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '-3.204e-02'
+network.layer3.1.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.1.bn3.running_mean:
+  device: cpu
+  max: '8.182e-02'
+  mean: '-1.315e-03'
+  min: '-8.96e-02'
+  shape:
+  - 1024
+  sum: '-1.346e+00'
+network.layer3.1.bn3.running_var:
+  device: cpu
+  max: '9.418e-01'
+  mean: '9.183e-01'
+  min: '9.118e-01'
+  shape:
+  - 1024
+  sum: '9.403e+02'
+network.layer3.1.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.1.conv1.weight:
+  device: cpu
+  max: '4.153e-01'
+  mean: '1.329e-05'
+  min: '-3.719e-01'
+  shape:
+  - 256
+  - 1024
+  - 1
+  - 1
+  sum: '3.484e+00'
+network.layer3.1.conv2.weight:
+  device: cpu
+  max: '1.319e-01'
+  mean: '1.791e-05'
+  min: '-1.378e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '1.056e+01'
+network.layer3.1.conv3.weight:
+  device: cpu
+  max: '2.061e-01'
+  mean: '-1.316e-04'
+  min: '-1.981e-01'
+  shape:
+  - 1024
+  - 256
+  - 1
+  - 1
+  sum: '-3.45e+01'
+network.layer3.2.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-2.343e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-5.999e-03'
+network.layer3.2.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.2.bn1.running_mean:
+  device: cpu
+  max: '5.523e-01'
+  mean: '-9.025e-03'
+  min: '-5.594e-01'
+  shape:
+  - 256
+  sum: '-2.310e+00'
+network.layer3.2.bn1.running_var:
+  device: cpu
+  max: '3.359e+00'
+  mean: '1.779e+00'
+  min: '1.495e+00'
+  shape:
+  - 256
+  sum: '4.555e+02'
+network.layer3.2.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.2.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '3.123e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '7.995e-03'
+network.layer3.2.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.2.bn2.running_mean:
+  device: cpu
+  max: '1.102e-01'
+  mean: '1.499e-03'
+  min: '-1.175e-01'
+  shape:
+  - 256
+  sum: '3.837e-01'
+network.layer3.2.bn2.running_var:
+  device: cpu
+  max: '1.042e+00'
+  mean: '9.695e-01'
+  min: '9.541e-01'
+  shape:
+  - 256
+  sum: '2.482e+02'
+network.layer3.2.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.2.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-4.489e-05'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '-4.597e-02'
+network.layer3.2.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.2.bn3.running_mean:
+  device: cpu
+  max: '9.134e-02'
+  mean: '-6.931e-05'
+  min: '-8.017e-02'
+  shape:
+  - 1024
+  sum: '-7.098e-02'
+network.layer3.2.bn3.running_var:
+  device: cpu
+  max: '9.484e-01'
+  mean: '9.182e-01'
+  min: '9.111e-01'
+  shape:
+  - 1024
+  sum: '9.403e+02'
+network.layer3.2.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.2.conv1.weight:
+  device: cpu
+  max: '4.003e-01'
+  mean: '-1.188e-04'
+  min: '-4.279e-01'
+  shape:
+  - 256
+  - 1024
+  - 1
+  - 1
+  sum: '-3.115e+01'
+network.layer3.2.conv2.weight:
+  device: cpu
+  max: '1.507e-01'
+  mean: '2.497e-05'
+  min: '-1.388e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '1.473e+01'
+network.layer3.2.conv3.weight:
+  device: cpu
+  max: '1.948e-01'
+  mean: '-3.24e-06'
+  min: '-1.997e-01'
+  shape:
+  - 1024
+  - 256
+  - 1
+  - 1
+  sum: '-8.493e-01'
+network.layer3.3.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.250e-04'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-3.201e-02'
+network.layer3.3.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.3.bn1.running_mean:
+  device: cpu
+  max: '9.693e-01'
+  mean: '-2.467e-02'
+  min: '-6.892e-01'
+  shape:
+  - 256
+  sum: '-6.315e+00'
+network.layer3.3.bn1.running_var:
+  device: cpu
+  max: '3.249e+00'
+  mean: '2.073e+00'
+  min: '1.752e+00'
+  shape:
+  - 256
+  sum: '5.308e+02'
+network.layer3.3.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.3.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-6.25e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-1.6e-02'
+network.layer3.3.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.3.bn2.running_mean:
+  device: cpu
+  max: '1.105e-01'
+  mean: '8.842e-04'
+  min: '-1.491e-01'
+  shape:
+  - 256
+  sum: '2.263e-01'
+network.layer3.3.bn2.running_var:
+  device: cpu
+  max: '1.046e+00'
+  mean: '9.7e-01'
+  min: '9.524e-01'
+  shape:
+  - 256
+  sum: '2.483e+02'
+network.layer3.3.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.3.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '1.955e-06'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '2.002e-03'
+network.layer3.3.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.3.bn3.running_mean:
+  device: cpu
+  max: '7.943e-02'
+  mean: '9.128e-04'
+  min: '-1.157e-01'
+  shape:
+  - 1024
+  sum: '9.347e-01'
+network.layer3.3.bn3.running_var:
+  device: cpu
+  max: '9.536e-01'
+  mean: '9.183e-01'
+  min: '9.116e-01'
+  shape:
+  - 1024
+  sum: '9.404e+02'
+network.layer3.3.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.3.conv1.weight:
+  device: cpu
+  max: '4.280e-01'
+  mean: '-2.251e-04'
+  min: '-3.926e-01'
+  shape:
+  - 256
+  - 1024
+  - 1
+  - 1
+  sum: '-5.901e+01'
+network.layer3.3.conv2.weight:
+  device: cpu
+  max: '1.375e-01'
+  mean: '3.005e-05'
+  min: '-1.374e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '1.772e+01'
+network.layer3.3.conv3.weight:
+  device: cpu
+  max: '2.021e-01'
+  mean: '1.104e-04'
+  min: '-2.052e-01'
+  shape:
+  - 1024
+  - 256
+  - 1
+  - 1
+  sum: '2.893e+01'
+network.layer3.4.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '7.797e-06'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '1.996e-03'
+network.layer3.4.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.4.bn1.running_mean:
+  device: cpu
+  max: '7.271e-01'
+  mean: '-2.522e-02'
+  min: '-8.967e-01'
+  shape:
+  - 256
+  sum: '-6.455e+00'
+network.layer3.4.bn1.running_var:
+  device: cpu
+  max: '5.281e+00'
+  mean: '2.465e+00'
+  min: '1.899e+00'
+  shape:
+  - 256
+  sum: '6.31e+02'
+network.layer3.4.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.4.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-7.793e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-1.995e-02'
+network.layer3.4.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.4.bn2.running_mean:
+  device: cpu
+  max: '1.438e-01'
+  mean: '-1.472e-03'
+  min: '-1.764e-01'
+  shape:
+  - 256
+  sum: '-3.768e-01'
+network.layer3.4.bn2.running_var:
+  device: cpu
+  max: '1.078e+00'
+  mean: '9.699e-01'
+  min: '9.490e-01'
+  shape:
+  - 256
+  sum: '2.483e+02'
+network.layer3.4.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.4.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.120e-05'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '-3.195e-02'
+network.layer3.4.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.4.bn3.running_mean:
+  device: cpu
+  max: '8.281e-02'
+  mean: '8.824e-04'
+  min: '-8.698e-02'
+  shape:
+  - 1024
+  sum: '9.036e-01'
+network.layer3.4.bn3.running_var:
+  device: cpu
+  max: '9.537e-01'
+  mean: '9.183e-01'
+  min: '9.102e-01'
+  shape:
+  - 1024
+  sum: '9.404e+02'
+network.layer3.4.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.4.conv1.weight:
+  device: cpu
+  max: '3.978e-01'
+  mean: '-2.200e-04'
+  min: '-3.861e-01'
+  shape:
+  - 256
+  - 1024
+  - 1
+  - 1
+  sum: '-5.768e+01'
+network.layer3.4.conv2.weight:
+  device: cpu
+  max: '1.382e-01'
+  mean: '-1.914e-05'
+  min: '-1.370e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '-1.129e+01'
+network.layer3.4.conv3.weight:
+  device: cpu
+  max: '2.110e-01'
+  mean: '9.864e-05'
+  min: '-2.042e-01'
+  shape:
+  - 1024
+  - 256
+  - 1
+  - 1
+  sum: '2.586e+01'
+network.layer3.5.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-4.688e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-1.200e-02'
+network.layer3.5.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.5.bn1.running_mean:
+  device: cpu
+  max: '9.884e-01'
+  mean: '5.432e-03'
+  min: '-9.654e-01'
+  shape:
+  - 256
+  sum: '1.391e+00'
+network.layer3.5.bn1.running_var:
+  device: cpu
+  max: '7.453e+00'
+  mean: '2.781e+00'
+  min: '2.145e+00'
+  shape:
+  - 256
+  sum: '7.120e+02'
+network.layer3.5.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.560e+02'
+network.layer3.5.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.904e-05'
+  min: '-1.e-03'
+  shape:
+  - 256
+  sum: '-9.994e-03'
+network.layer3.5.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.5.bn2.running_mean:
+  device: cpu
+  max: '1.454e-01'
+  mean: '2.831e-03'
+  min: '-1.070e-01'
+  shape:
+  - 256
+  sum: '7.248e-01'
+network.layer3.5.bn2.running_var:
+  device: cpu
+  max: '1.043e+00'
+  mean: '9.699e-01'
+  min: '9.54e-01'
+  shape:
+  - 256
+  sum: '2.483e+02'
+network.layer3.5.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 256
+  sum: '2.56e+02'
+network.layer3.5.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '1.366e-05'
+  min: '-1.e-03'
+  shape:
+  - 1024
+  sum: '1.399e-02'
+network.layer3.5.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer3.5.bn3.running_mean:
+  device: cpu
+  max: '7.603e-02'
+  mean: '-2.997e-04'
+  min: '-9.626e-02'
+  shape:
+  - 1024
+  sum: '-3.069e-01'
+network.layer3.5.bn3.running_var:
+  device: cpu
+  max: '9.527e-01'
+  mean: '9.182e-01'
+  min: '9.114e-01'
+  shape:
+  - 1024
+  sum: '9.402e+02'
+network.layer3.5.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 1024
+  sum: '1.024e+03'
+network.layer3.5.conv1.weight:
+  device: cpu
+  max: '3.742e-01'
+  mean: '4.989e-05'
+  min: '-4.046e-01'
+  shape:
+  - 256
+  - 1024
+  - 1
+  - 1
+  sum: '1.308e+01'
+network.layer3.5.conv2.weight:
+  device: cpu
+  max: '1.392e-01'
+  mean: '5.371e-05'
+  min: '-1.334e-01'
+  shape:
+  - 256
+  - 256
+  - 3
+  - 3
+  sum: '3.168e+01'
+network.layer3.5.conv3.weight:
+  device: cpu
+  max: '2.13e-01'
+  mean: '-1.377e-05'
+  min: '-2.005e-01'
+  shape:
+  - 1024
+  - 256
+  - 1
+  - 1
+  sum: '-3.609e+00'
+network.layer4.0.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-8.51e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-4.357e-02'
+network.layer4.0.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.bn1.running_mean:
+  device: cpu
+  max: '9.35e-01'
+  mean: '2.956e-02'
+  min: '-7.902e-01'
+  shape:
+  - 512
+  sum: '1.513e+01'
+network.layer4.0.bn1.running_var:
+  device: cpu
+  max: '4.638e+00'
+  mean: '2.018e+00'
+  min: '1.623e+00'
+  shape:
+  - 512
+  sum: '1.033e+03'
+network.layer4.0.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.0.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '3.51e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '1.797e-02'
+network.layer4.0.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.bn2.running_mean:
+  device: cpu
+  max: '1.114e-01'
+  mean: '-1.326e-03'
+  min: '-1.546e-01'
+  shape:
+  - 512
+  sum: '-6.789e-01'
+network.layer4.0.bn2.running_var:
+  device: cpu
+  max: '1.118e+00'
+  mean: '9.738e-01'
+  min: '9.492e-01'
+  shape:
+  - 512
+  sum: '4.986e+02'
+network.layer4.0.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.0.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.142e-04'
+  min: '-1.e-03'
+  shape:
+  - 2048
+  sum: '-2.34e-01'
+network.layer4.0.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.bn3.running_mean:
+  device: cpu
+  max: '1.039e-01'
+  mean: '1.895e-04'
+  min: '-8.169e-02'
+  shape:
+  - 2048
+  sum: '3.882e-01'
+network.layer4.0.bn3.running_var:
+  device: cpu
+  max: '9.551e-01'
+  mean: '9.185e-01'
+  min: '9.118e-01'
+  shape:
+  - 2048
+  sum: '1.881e+03'
+network.layer4.0.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 2048
+  sum: '2.048e+03'
+network.layer4.0.conv1.weight:
+  device: cpu
+  max: '2.863e-01'
+  mean: '2.204e-04'
+  min: '-2.954e-01'
+  shape:
+  - 512
+  - 1024
+  - 1
+  - 1
+  sum: '1.155e+02'
+network.layer4.0.conv2.weight:
+  device: cpu
+  max: '1.032e-01'
+  mean: '-4.406e-06'
+  min: '-1.125e-01'
+  shape:
+  - 512
+  - 512
+  - 3
+  - 3
+  sum: '-1.04e+01'
+network.layer4.0.conv3.weight:
+  device: cpu
+  max: '1.459e-01'
+  mean: '1.508e-05'
+  min: '-1.462e-01'
+  shape:
+  - 2048
+  - 512
+  - 1
+  - 1
+  sum: '1.582e+01'
+network.layer4.0.downsample.0.weight:
+  device: cpu
+  max: '1.653e-01'
+  mean: '1.025e-05'
+  min: '-1.527e-01'
+  shape:
+  - 2048
+  - 1024
+  - 1
+  - 1
+  sum: '2.15e+01'
+network.layer4.0.downsample.1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.142e-04'
+  min: '-1.e-03'
+  shape:
+  - 2048
+  sum: '-2.34e-01'
+network.layer4.0.downsample.1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.0.downsample.1.running_mean:
+  device: cpu
+  max: '5.283e-01'
+  mean: '1.796e-03'
+  min: '-4.676e-01'
+  shape:
+  - 2048
+  sum: '3.678e+00'
+network.layer4.0.downsample.1.running_var:
+  device: cpu
+  max: '1.839e+00'
+  mean: '1.177e+00'
+  min: '1.076e+00'
+  shape:
+  - 2048
+  sum: '2.411e+03'
+network.layer4.0.downsample.1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 2048
+  sum: '2.048e+03'
+network.layer4.1.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.126e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-1.600e-02'
+network.layer4.1.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.1.bn1.running_mean:
+  device: cpu
+  max: '4.242e-01'
+  mean: '1.178e-02'
+  min: '-5.965e-01'
+  shape:
+  - 512
+  sum: '6.033e+00'
+network.layer4.1.bn1.running_var:
+  device: cpu
+  max: '2.345e+00'
+  mean: '1.484e+00'
+  min: '1.308e+00'
+  shape:
+  - 512
+  sum: '7.598e+02'
+network.layer4.1.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.1.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-7.815e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-4.001e-02'
+network.layer4.1.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.1.bn2.running_mean:
+  device: cpu
+  max: '1.691e-01'
+  mean: '-2.182e-03'
+  min: '-1.217e-01'
+  shape:
+  - 512
+  sum: '-1.117e+00'
+network.layer4.1.bn2.running_var:
+  device: cpu
+  max: '1.041e+00'
+  mean: '9.681e-01'
+  min: '9.495e-01'
+  shape:
+  - 512
+  sum: '4.957e+02'
+network.layer4.1.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.1.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.143e-04'
+  min: '-1.e-03'
+  shape:
+  - 2048
+  sum: '-2.340e-01'
+network.layer4.1.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.1.bn3.running_mean:
+  device: cpu
+  max: '8.294e-02'
+  mean: '6.182e-05'
+  min: '-9.734e-02'
+  shape:
+  - 2048
+  sum: '1.266e-01'
+network.layer4.1.bn3.running_var:
+  device: cpu
+  max: '9.518e-01'
+  mean: '9.184e-01'
+  min: '9.122e-01'
+  shape:
+  - 2048
+  sum: '1.881e+03'
+network.layer4.1.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 2048
+  sum: '2.048e+03'
+network.layer4.1.conv1.weight:
+  device: cpu
+  max: '3.303e-01'
+  mean: '1.108e-04'
+  min: '-3.103e-01'
+  shape:
+  - 512
+  - 2048
+  - 1
+  - 1
+  sum: '1.162e+02'
+network.layer4.1.conv2.weight:
+  device: cpu
+  max: '1.066e-01'
+  mean: '-8.026e-06'
+  min: '-1.133e-01'
+  shape:
+  - 512
+  - 512
+  - 3
+  - 3
+  sum: '-1.894e+01'
+network.layer4.1.conv3.weight:
+  device: cpu
+  max: '1.437e-01'
+  mean: '6.096e-06'
+  min: '-1.423e-01'
+  shape:
+  - 2048
+  - 512
+  - 1
+  - 1
+  sum: '6.392e+00'
+network.layer4.2.bn1.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-3.129e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '-1.602e-02'
+network.layer4.2.bn1.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.2.bn1.running_mean:
+  device: cpu
+  max: '5.355e-01'
+  mean: '-3.521e-03'
+  min: '-7.034e-01'
+  shape:
+  - 512
+  sum: '-1.803e+00'
+network.layer4.2.bn1.running_var:
+  device: cpu
+  max: '4.947e+00'
+  mean: '1.816e+00'
+  min: '1.495e+00'
+  shape:
+  - 512
+  sum: '9.300e+02'
+network.layer4.2.bn1.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.12e+02'
+network.layer4.2.bn2.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '6.635e-05'
+  min: '-1.e-03'
+  shape:
+  - 512
+  sum: '3.397e-02'
+network.layer4.2.bn2.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.2.bn2.running_mean:
+  device: cpu
+  max: '1.533e-01'
+  mean: '-5.166e-04'
+  min: '-1.150e-01'
+  shape:
+  - 512
+  sum: '-2.645e-01'
+network.layer4.2.bn2.running_var:
+  device: cpu
+  max: '1.048e+00'
+  mean: '9.674e-01'
+  min: '9.466e-01'
+  shape:
+  - 512
+  sum: '4.953e+02'
+network.layer4.2.bn2.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '1.000e+00'
+  min: '9.990e-01'
+  shape:
+  - 512
+  sum: '5.120e+02'
+network.layer4.2.bn3.bias:
+  device: cpu
+  max: '1.e-03'
+  mean: '-1.162e-04'
+  min: '-1.e-03'
+  shape:
+  - 2048
+  sum: '-2.38e-01'
+network.layer4.2.bn3.num_batches_tracked:
+  device: cpu
+  max: 1
+  mean: '1.e+00'
+  min: 1
+  shape: []
+  sum: 1
+network.layer4.2.bn3.running_mean:
+  device: cpu
+  max: '8.291e-02'
+  mean: '-2.328e-04'
+  min: '-8.115e-02'
+  shape:
+  - 2048
+  sum: '-4.768e-01'
+network.layer4.2.bn3.running_var:
+  device: cpu
+  max: '9.555e-01'
+  mean: '9.185e-01'
+  min: '9.114e-01'
+  shape:
+  - 2048
+  sum: '1.881e+03'
+network.layer4.2.bn3.weight:
+  device: cpu
+  max: '1.001e+00'
+  mean: '9.999e-01'
+  min: '9.990e-01'
+  shape:
+  - 2048
+  sum: '2.048e+03'
+network.layer4.2.conv1.weight:
+  device: cpu
+  max: '2.976e-01'
+  mean: '-1.228e-05'
+  min: '-3.007e-01'
+  shape:
+  - 512
+  - 2048
+  - 1
+  - 1
+  sum: '-1.288e+01'
+network.layer4.2.conv2.weight:
+  device: cpu
+  max: '9.741e-02'
+  mean: '1.520e-07'
+  min: '-1.042e-01'
+  shape:
+  - 512
+  - 512
+  - 3
+  - 3
+  sum: '3.587e-01'
+network.layer4.2.conv3.weight:
+  device: cpu
+  max: '1.532e-01'
+  mean: '-5.868e-06'
+  min: '-1.502e-01'
+  shape:
+  - 2048
+  - 512
+  - 1
+  - 1
+  sum: '-6.153e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/cifar10_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/cifar10_jax_cnn_jax_image_classifier.yaml
index ff422c2a..6c11e727 100644
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/cifar10_jax_cnn_jax_image_classifier.yaml
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/cifar10_jax_cnn_jax_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.126e+00'
-  mean: '-6.179e-03'
+  mean: '6.869e-03'
   min: '-1.989e+00'
   shape:
   - 128
   - 3
   - 32
   - 32
-  sum: '-2.43e+03'
+  sum: '2.701e+03'
 batch.1:
   device: cuda:0
   max: 9
@@ -19,92 +19,92 @@ batch.1:
   sum: 583
 grads.network.params.0:
   device: cuda:0
-  max: '9.654e-03'
-  mean: '1.276e-03'
-  min: '-1.148e-02'
+  max: '1.033e-02'
+  mean: '1.787e-03'
+  min: '-1.095e-02'
   shape:
   - 32
-  sum: '4.083e-02'
+  sum: '5.719e-02'
 grads.network.params.1:
   device: cuda:0
-  max: '1.149e-02'
-  mean: '5.030e-04'
-  min: '-1.473e-02'
+  max: '1.470e-02'
+  mean: '-5.644e-05'
+  min: '-1.356e-02'
   shape:
   - 3
   - 3
   - 3
   - 32
-  sum: '4.346e-01'
+  sum: '-4.876e-02'
 grads.network.params.2:
   device: cuda:0
-  max: '1.680e-02'
-  mean: '1.566e-03'
-  min: '-7.296e-03'
+  max: '1.36e-02'
+  mean: '1.604e-03'
+  min: '-8.109e-03'
   shape:
   - 64
-  sum: '1.002e-01'
+  sum: '1.026e-01'
 grads.network.params.3:
   device: cuda:0
-  max: '2.507e-02'
-  mean: '4.631e-04'
-  min: '-2.280e-02'
+  max: '2.499e-02'
+  mean: '5.008e-04'
+  min: '-2.416e-02'
   shape:
   - 3
   - 3
   - 32
   - 64
-  sum: '8.536e+00'
+  sum: '9.231e+00'
 grads.network.params.4:
   device: cuda:0
-  max: '1.025e-02'
-  mean: '1.384e-04'
-  min: '-1.082e-02'
+  max: '9.955e-03'
+  mean: '3.320e-04'
+  min: '-8.475e-03'
   shape:
   - 256
-  sum: '3.542e-02'
+  sum: '8.5e-02'
 grads.network.params.5:
   device: cuda:0
-  max: '3.064e-02'
-  mean: '3.315e-05'
-  min: '-2.379e-02'
+  max: '2.433e-02'
+  mean: '8.346e-05'
+  min: '-2.655e-02'
   shape:
   - 4096
   - 256
-  sum: '3.476e+01'
+  sum: '8.751e+01'
 grads.network.params.6:
   device: cuda:0
-  max: '2.984e-02'
-  mean: '-5.588e-10'
-  min: '-2.597e-02'
+  max: '3.249e-02'
+  mean: '-7.451e-10'
+  min: '-2.593e-02'
   shape:
   - 10
-  sum: '-5.588e-09'
+  sum: '-7.451e-09'
 grads.network.params.7:
   device: cuda:0
-  max: '4.361e-02'
-  mean: '-2.154e-10'
-  min: '-4.662e-02'
+  max: '3.762e-02'
+  mean: '-1.673e-10'
+  min: '-4.220e-02'
   shape:
   - 256
   - 10
-  sum: '-5.513e-07'
+  sum: '-4.284e-07'
 outputs.logits:
   device: cuda:0
-  max: '9.608e-01'
-  mean: '1.186e-01'
-  min: '-7.613e-01'
+  max: '1.041e+00'
+  mean: '1.176e-01'
+  min: '-5.904e-01'
   shape:
   - 128
   - 10
-  sum: '1.519e+02'
+  sum: '1.506e+02'
 outputs.loss:
   device: cuda:0
-  max: '2.341e+00'
-  mean: '2.341e+00'
-  min: '2.341e+00'
+  max: '2.358e+00'
+  mean: '2.358e+00'
+  min: '2.358e+00'
   shape: []
-  sum: '2.341e+00'
+  sum: '2.358e+00'
 outputs.y:
   device: cuda:0
   max: 9
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/cifar10_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/cifar10_jax_fcnet_jax_image_classifier.yaml
index 2fe6e1fa..9276335a 100644
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/cifar10_jax_fcnet_jax_image_classifier.yaml
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/cifar10_jax_fcnet_jax_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.126e+00'
-  mean: '-6.179e-03'
+  mean: '6.869e-03'
   min: '-1.989e+00'
   shape:
   - 128
   - 3
   - 32
   - 32
-  sum: '-2.43e+03'
+  sum: '2.701e+03'
 batch.1:
   device: cuda:0
   max: 9
@@ -19,54 +19,54 @@ batch.1:
   sum: 583
 grads.network.params.0:
   device: cuda:0
-  max: '1.552e-02'
-  mean: '8.602e-04'
-  min: '-9.862e-03'
+  max: '1.519e-02'
+  mean: '6.641e-04'
+  min: '-1.13e-02'
   shape:
   - 256
-  sum: '2.202e-01'
+  sum: '1.700e-01'
 grads.network.params.1:
   device: cuda:0
-  max: '2.677e-02'
-  mean: '1.968e-05'
-  min: '-2.576e-02'
+  max: '2.499e-02'
+  mean: '4.967e-05'
+  min: '-2.296e-02'
   shape:
   - 3072
   - 256
-  sum: '1.548e+01'
+  sum: '3.906e+01'
 grads.network.params.2:
   device: cuda:0
-  max: '6.868e-02'
+  max: '6.439e-02'
   mean: '0.e+00'
-  min: '-3.458e-02'
+  min: '-3.123e-02'
   shape:
   - 10
   sum: '0.e+00'
 grads.network.params.3:
   device: cuda:0
-  max: '1.497e-01'
-  mean: '-2.445e-10'
-  min: '-1.415e-01'
+  max: '1.444e-01'
+  mean: '-9.313e-11'
+  min: '-1.493e-01'
   shape:
   - 256
   - 10
-  sum: '-6.258e-07'
+  sum: '-2.384e-07'
 outputs.logits:
   device: cuda:0
-  max: '2.380e+00'
-  mean: '5.809e-02'
-  min: '-3.135e+00'
+  max: '2.930e+00'
+  mean: '9.066e-02'
+  min: '-3.197e+00'
   shape:
   - 128
   - 10
-  sum: '7.436e+01'
+  sum: '1.160e+02'
 outputs.loss:
   device: cuda:0
-  max: '2.466e+00'
-  mean: '2.466e+00'
-  min: '2.466e+00'
+  max: '2.450e+00'
+  mean: '2.450e+00'
+  min: '2.450e+00'
   shape: []
-  sum: '2.466e+00'
+  sum: '2.450e+00'
 outputs.y:
   device: cuda:0
   max: 9
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/fashion_mnist_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/fashion_mnist_jax_cnn_jax_image_classifier.yaml
index 7b7a7623..4bfb9392 100644
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/fashion_mnist_jax_cnn_jax_image_classifier.yaml
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/fashion_mnist_jax_cnn_jax_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.821e+00'
-  mean: '4.822e-01'
+  mean: '4.772e-01'
   min: '-4.242e-01'
   shape:
   - 128
   - 1
   - 28
   - 28
-  sum: '4.839e+04'
+  sum: '4.789e+04'
 batch.1:
   device: cuda:0
   max: 9
@@ -19,92 +19,92 @@ batch.1:
   sum: 583
 grads.network.params.0:
   device: cuda:0
-  max: '1.949e-02'
-  mean: '4.526e-03'
-  min: '-1.615e-02'
+  max: '1.939e-02'
+  mean: '3.894e-03'
+  min: '-1.937e-02'
   shape:
   - 32
-  sum: '1.448e-01'
+  sum: '1.246e-01'
 grads.network.params.1:
   device: cuda:0
-  max: '4.36e-02'
-  mean: '5.924e-03'
-  min: '-3.013e-02'
+  max: '4.019e-02'
+  mean: '5.364e-03'
+  min: '-3.658e-02'
   shape:
   - 3
   - 3
   - 1
   - 32
-  sum: '1.706e+00'
+  sum: '1.545e+00'
 grads.network.params.2:
   device: cuda:0
-  max: '2.734e-02'
-  mean: '1.847e-03'
-  min: '-1.76e-02'
+  max: '2.629e-02'
+  mean: '2.084e-03'
+  min: '-1.461e-02'
   shape:
   - 64
-  sum: '1.182e-01'
+  sum: '1.334e-01'
 grads.network.params.3:
   device: cuda:0
-  max: '6.099e-02'
-  mean: '1.127e-03'
-  min: '-5.833e-02'
+  max: '6.494e-02'
+  mean: '1.452e-03'
+  min: '-4.242e-02'
   shape:
   - 3
   - 3
   - 32
   - 64
-  sum: '2.077e+01'
+  sum: '2.676e+01'
 grads.network.params.4:
   device: cuda:0
-  max: '2.451e-02'
-  mean: '1.065e-03'
-  min: '-1.999e-02'
+  max: '2.387e-02'
+  mean: '1.059e-03'
+  min: '-1.772e-02'
   shape:
   - 256
-  sum: '2.727e-01'
+  sum: '2.711e-01'
 grads.network.params.5:
   device: cuda:0
-  max: '7.691e-02'
-  mean: '3.075e-04'
-  min: '-6.106e-02'
+  max: '7.960e-02'
+  mean: '3.147e-04'
+  min: '-5.898e-02'
   shape:
   - 3136
   - 256
-  sum: '2.469e+02'
+  sum: '2.526e+02'
 grads.network.params.6:
   device: cuda:0
-  max: '5.898e-02'
-  mean: '-1.863e-09'
-  min: '-7.022e-02'
+  max: '6.150e-02'
+  mean: '0.e+00'
+  min: '-6.966e-02'
   shape:
   - 10
-  sum: '-1.863e-08'
+  sum: '0.e+00'
 grads.network.params.7:
   device: cuda:0
-  max: '1.382e-01'
-  mean: '-1.775e-10'
-  min: '-1.376e-01'
+  max: '1.175e-01'
+  mean: '-7.567e-11'
+  min: '-1.294e-01'
   shape:
   - 256
   - 10
-  sum: '-4.545e-07'
+  sum: '-1.937e-07'
 outputs.logits:
   device: cuda:0
-  max: '1.032e+00'
-  mean: '-1.1e-02'
-  min: '-9.602e-01'
+  max: '9.607e-01'
+  mean: '-2.087e-02'
+  min: '-1.008e+00'
   shape:
   - 128
   - 10
-  sum: '-1.408e+01'
+  sum: '-2.671e+01'
 outputs.loss:
   device: cuda:0
-  max: '2.385e+00'
-  mean: '2.385e+00'
-  min: '2.385e+00'
+  max: '2.381e+00'
+  mean: '2.381e+00'
+  min: '2.381e+00'
   shape: []
-  sum: '2.385e+00'
+  sum: '2.381e+00'
 outputs.y:
   device: cuda:0
   max: 9
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/fashion_mnist_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/fashion_mnist_jax_fcnet_jax_image_classifier.yaml
index 7a36defc..0d605ef3 100644
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/fashion_mnist_jax_fcnet_jax_image_classifier.yaml
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/fashion_mnist_jax_fcnet_jax_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.821e+00'
-  mean: '4.822e-01'
+  mean: '4.772e-01'
   min: '-4.242e-01'
   shape:
   - 128
   - 1
   - 28
   - 28
-  sum: '4.839e+04'
+  sum: '4.789e+04'
 batch.1:
   device: cuda:0
   max: 9
@@ -19,54 +19,54 @@ batch.1:
   sum: 583
 grads.network.params.0:
   device: cuda:0
-  max: '2.188e-02'
-  mean: '8.325e-04'
-  min: '-2.096e-02'
+  max: '2.169e-02'
+  mean: '6.964e-04'
+  min: '-1.89e-02'
   shape:
   - 256
-  sum: '2.131e-01'
+  sum: '1.783e-01'
 grads.network.params.1:
   device: cuda:0
-  max: '5.304e-02'
-  mean: '4.879e-04'
-  min: '-4.886e-02'
+  max: '5.238e-02'
+  mean: '3.488e-04'
+  min: '-4.438e-02'
   shape:
   - 784
   - 256
-  sum: '9.792e+01'
+  sum: '7.001e+01'
 grads.network.params.2:
   device: cuda:0
-  max: '1.375e-01'
+  max: '1.382e-01'
   mean: '0.e+00'
-  min: '-9.162e-02'
+  min: '-9.016e-02'
   shape:
   - 10
   sum: '0.e+00'
 grads.network.params.3:
   device: cuda:0
-  max: '3.990e-01'
-  mean: '-1.106e-10'
-  min: '-2.054e-01'
+  max: '4.029e-01'
+  mean: '-5.122e-10'
+  min: '-2.145e-01'
   shape:
   - 256
   - 10
-  sum: '-2.831e-07'
+  sum: '-1.311e-06'
 outputs.logits:
   device: cuda:0
-  max: '2.656e+00'
-  mean: '2.355e-02'
-  min: '-2.715e+00'
+  max: '2.481e+00'
+  mean: '1.568e-02'
+  min: '-2.414e+00'
   shape:
   - 128
   - 10
-  sum: '3.015e+01'
+  sum: '2.007e+01'
 outputs.loss:
   device: cuda:0
-  max: '2.554e+00'
-  mean: '2.554e+00'
-  min: '2.554e+00'
+  max: '2.495e+00'
+  mean: '2.495e+00'
+  min: '2.495e+00'
   shape: []
-  sum: '2.554e+00'
+  sum: '2.495e+00'
 outputs.y:
   device: cuda:0
   max: 9
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/mnist_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/mnist_jax_cnn_jax_image_classifier.yaml
index d41f869b..e797effc 100644
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/mnist_jax_cnn_jax_image_classifier.yaml
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/mnist_jax_cnn_jax_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.821e+00'
-  mean: '1.432e-02'
+  mean: '1.477e-02'
   min: '-4.242e-01'
   shape:
   - 128
   - 1
   - 28
   - 28
-  sum: '1.437e+03'
+  sum: '1.482e+03'
 batch.1:
   device: cuda:0
   max: 9
@@ -19,92 +19,92 @@ batch.1:
   sum: 543
 grads.network.params.0:
   device: cuda:0
-  max: '1.65e-02'
-  mean: '2.109e-03'
-  min: '-8.628e-03'
+  max: '1.631e-02'
+  mean: '1.768e-03'
+  min: '-9.400e-03'
   shape:
   - 32
-  sum: '6.748e-02'
+  sum: '5.657e-02'
 grads.network.params.1:
   device: cuda:0
-  max: '1.893e-02'
-  mean: '-1.55e-05'
-  min: '-1.627e-02'
+  max: '2.339e-02'
+  mean: '1.541e-03'
+  min: '-1.485e-02'
   shape:
   - 3
   - 3
   - 1
   - 32
-  sum: '-4.463e-03'
+  sum: '4.439e-01'
 grads.network.params.2:
   device: cuda:0
-  max: '2.053e-02'
-  mean: '1.196e-03'
-  min: '-1.783e-02'
+  max: '1.839e-02'
+  mean: '1.279e-03'
+  min: '-1.943e-02'
   shape:
   - 64
-  sum: '7.653e-02'
+  sum: '8.189e-02'
 grads.network.params.3:
   device: cuda:0
-  max: '2.25e-02'
-  mean: '3.613e-04'
-  min: '-2.352e-02'
+  max: '2.182e-02'
+  mean: '8.145e-04'
+  min: '-2.273e-02'
   shape:
   - 3
   - 3
   - 32
   - 64
-  sum: '6.659e+00'
+  sum: '1.501e+01'
 grads.network.params.4:
   device: cuda:0
-  max: '2.231e-02'
-  mean: '2.332e-04'
-  min: '-2.018e-02'
+  max: '2.015e-02'
+  mean: '4.503e-04'
+  min: '-1.649e-02'
   shape:
   - 256
-  sum: '5.970e-02'
+  sum: '1.153e-01'
 grads.network.params.5:
   device: cuda:0
-  max: '5.356e-02'
-  mean: '3.131e-05'
-  min: '-4.563e-02'
+  max: '4.575e-02'
+  mean: '8.089e-05'
+  min: '-4.015e-02'
   shape:
   - 3136
   - 256
-  sum: '2.514e+01'
+  sum: '6.494e+01'
 grads.network.params.6:
   device: cuda:0
-  max: '6.484e-02'
-  mean: '-1.490e-09'
-  min: '-8.046e-02'
+  max: '6.867e-02'
+  mean: '-7.451e-10'
+  min: '-7.932e-02'
   shape:
   - 10
-  sum: '-1.490e-08'
+  sum: '-7.451e-09'
 grads.network.params.7:
   device: cuda:0
-  max: '7.496e-02'
-  mean: '-3.361e-10'
-  min: '-8.565e-02'
+  max: '7.035e-02'
+  mean: '-1.193e-10'
+  min: '-7.68e-02'
   shape:
   - 256
   - 10
-  sum: '-8.605e-07'
+  sum: '-3.055e-07'
 outputs.logits:
   device: cuda:0
-  max: '8.092e-01'
-  mean: '-2.764e-02'
-  min: '-1.135e+00'
+  max: '8.371e-01'
+  mean: '-2.84e-02'
+  min: '-1.107e+00'
   shape:
   - 128
   - 10
-  sum: '-3.538e+01'
+  sum: '-3.635e+01'
 outputs.loss:
   device: cuda:0
-  max: '2.303e+00'
-  mean: '2.303e+00'
-  min: '2.303e+00'
+  max: '2.315e+00'
+  mean: '2.315e+00'
+  min: '2.315e+00'
   shape: []
-  sum: '2.303e+00'
+  sum: '2.315e+00'
 outputs.y:
   device: cuda:0
   max: 9
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/mnist_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/mnist_jax_fcnet_jax_image_classifier.yaml
index b1219522..0e6d868f 100644
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/mnist_jax_fcnet_jax_image_classifier.yaml
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_backward_pass_is_reproducible/mnist_jax_fcnet_jax_image_classifier.yaml
@@ -1,14 +1,14 @@
 batch.0:
   device: cuda:0
   max: '2.821e+00'
-  mean: '1.432e-02'
+  mean: '1.477e-02'
   min: '-4.242e-01'
   shape:
   - 128
   - 1
   - 28
   - 28
-  sum: '1.437e+03'
+  sum: '1.482e+03'
 batch.1:
   device: cuda:0
   max: 9
@@ -19,54 +19,54 @@ batch.1:
   sum: 543
 grads.network.params.0:
   device: cuda:0
-  max: '1.386e-02'
-  mean: '8.019e-04'
-  min: '-1.326e-02'
+  max: '1.272e-02'
+  mean: '7.16e-04'
+  min: '-1.135e-02'
   shape:
   - 256
-  sum: '2.053e-01'
+  sum: '1.833e-01'
 grads.network.params.1:
   device: cuda:0
-  max: '3.122e-02'
-  mean: '-1.002e-04'
-  min: '-3.579e-02'
+  max: '3.092e-02'
+  mean: '-1.042e-04'
+  min: '-2.940e-02'
   shape:
   - 784
   - 256
-  sum: '-2.012e+01'
+  sum: '-2.092e+01'
 grads.network.params.2:
   device: cuda:0
-  max: '4.549e-02'
-  mean: '0.e+00'
-  min: '-7.537e-02'
+  max: '4.535e-02'
+  mean: '7.451e-10'
+  min: '-7.950e-02'
   shape:
   - 10
-  sum: '0.e+00'
+  sum: '7.451e-09'
 grads.network.params.3:
   device: cuda:0
-  max: '7.07e-02'
-  mean: '-5.821e-11'
-  min: '-1.064e-01'
+  max: '8.090e-02'
+  mean: '1.339e-10'
+  min: '-1.129e-01'
   shape:
   - 256
   - 10
-  sum: '-1.490e-07'
+  sum: '3.427e-07'
 outputs.logits:
   device: cuda:0
-  max: '1.85e+00'
-  mean: '6.708e-02'
-  min: '-1.919e+00'
+  max: '2.035e+00'
+  mean: '9.444e-02'
+  min: '-1.669e+00'
   shape:
   - 128
   - 10
-  sum: '8.586e+01'
+  sum: '1.209e+02'
 outputs.loss:
   device: cuda:0
-  max: '2.398e+00'
-  mean: '2.398e+00'
-  min: '2.398e+00'
+  max: '2.440e+00'
+  mean: '2.440e+00'
+  min: '2.440e+00'
   shape: []
-  sum: '2.398e+00'
+  sum: '2.440e+00'
 outputs.y:
   device: cuda:0
   max: 9
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/cifar10_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/cifar10_jax_cnn_jax_image_classifier.yaml
new file mode 100644
index 00000000..74b4ba26
--- /dev/null
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/cifar10_jax_cnn_jax_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 3
+  - 32
+  - 32
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 10
+  sum: '0.e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/cifar10_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/cifar10_jax_fcnet_jax_image_classifier.yaml
new file mode 100644
index 00000000..74b4ba26
--- /dev/null
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/cifar10_jax_fcnet_jax_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 3
+  - 32
+  - 32
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 10
+  sum: '0.e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/fashion_mnist_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/fashion_mnist_jax_cnn_jax_image_classifier.yaml
new file mode 100644
index 00000000..a33c8328
--- /dev/null
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/fashion_mnist_jax_cnn_jax_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 1
+  - 28
+  - 28
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 10
+  sum: '0.e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/fashion_mnist_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/fashion_mnist_jax_fcnet_jax_image_classifier.yaml
new file mode 100644
index 00000000..a33c8328
--- /dev/null
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/fashion_mnist_jax_fcnet_jax_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 1
+  - 28
+  - 28
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 10
+  sum: '0.e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/mnist_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/mnist_jax_cnn_jax_image_classifier.yaml
new file mode 100644
index 00000000..a33c8328
--- /dev/null
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/mnist_jax_cnn_jax_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 1
+  - 28
+  - 28
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 10
+  sum: '0.e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/mnist_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/mnist_jax_fcnet_jax_image_classifier.yaml
new file mode 100644
index 00000000..a33c8328
--- /dev/null
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cpu/mnist_jax_fcnet_jax_image_classifier.yaml
@@ -0,0 +1,20 @@
+input.0:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 1
+  - 28
+  - 28
+  sum: '0.e+00'
+out:
+  device: cuda:0
+  max: '0.e+00'
+  mean: '0.e+00'
+  min: '0.e+00'
+  shape:
+  - 128
+  - 10
+  sum: '0.e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/cifar10_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/cifar10_jax_cnn_jax_image_classifier.yaml
deleted file mode 100644
index 196d0c55..00000000
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/cifar10_jax_cnn_jax_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.126e+00'
-  mean: '-6.179e-03'
-  min: '-1.989e+00'
-  shape:
-  - 128
-  - 3
-  - 32
-  - 32
-  sum: '-2.43e+03'
-out:
-  device: cuda:0
-  max: '9.608e-01'
-  mean: '1.186e-01'
-  min: '-7.613e-01'
-  shape:
-  - 128
-  - 10
-  sum: '1.519e+02'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/cifar10_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/cifar10_jax_fcnet_jax_image_classifier.yaml
deleted file mode 100644
index c73fe9ab..00000000
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/cifar10_jax_fcnet_jax_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.126e+00'
-  mean: '-6.179e-03'
-  min: '-1.989e+00'
-  shape:
-  - 128
-  - 3
-  - 32
-  - 32
-  sum: '-2.43e+03'
-out:
-  device: cuda:0
-  max: '2.380e+00'
-  mean: '5.809e-02'
-  min: '-3.135e+00'
-  shape:
-  - 128
-  - 10
-  sum: '7.436e+01'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/fashion_mnist_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/fashion_mnist_jax_cnn_jax_image_classifier.yaml
deleted file mode 100644
index da4a2d73..00000000
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/fashion_mnist_jax_cnn_jax_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.821e+00'
-  mean: '4.822e-01'
-  min: '-4.242e-01'
-  shape:
-  - 128
-  - 1
-  - 28
-  - 28
-  sum: '4.839e+04'
-out:
-  device: cuda:0
-  max: '1.032e+00'
-  mean: '-1.1e-02'
-  min: '-9.602e-01'
-  shape:
-  - 128
-  - 10
-  sum: '-1.408e+01'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/fashion_mnist_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/fashion_mnist_jax_fcnet_jax_image_classifier.yaml
deleted file mode 100644
index 7e489df5..00000000
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/fashion_mnist_jax_fcnet_jax_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.821e+00'
-  mean: '4.822e-01'
-  min: '-4.242e-01'
-  shape:
-  - 128
-  - 1
-  - 28
-  - 28
-  sum: '4.839e+04'
-out:
-  device: cuda:0
-  max: '2.656e+00'
-  mean: '2.355e-02'
-  min: '-2.715e+00'
-  shape:
-  - 128
-  - 10
-  sum: '3.015e+01'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/mnist_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/mnist_jax_cnn_jax_image_classifier.yaml
deleted file mode 100644
index 81a21836..00000000
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/mnist_jax_cnn_jax_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.821e+00'
-  mean: '1.432e-02'
-  min: '-4.242e-01'
-  shape:
-  - 128
-  - 1
-  - 28
-  - 28
-  sum: '1.437e+03'
-out:
-  device: cuda:0
-  max: '8.092e-01'
-  mean: '-2.764e-02'
-  min: '-1.135e+00'
-  shape:
-  - 128
-  - 10
-  sum: '-3.538e+01'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/mnist_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/mnist_jax_fcnet_jax_image_classifier.yaml
deleted file mode 100644
index 5659f1e9..00000000
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_forward_pass_is_reproducible/cuda/mnist_jax_fcnet_jax_image_classifier.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-input:
-  device: cuda:0
-  max: '2.821e+00'
-  mean: '1.432e-02'
-  min: '-4.242e-01'
-  shape:
-  - 128
-  - 1
-  - 28
-  - 28
-  sum: '1.437e+03'
-out:
-  device: cuda:0
-  max: '1.85e+00'
-  mean: '6.708e-02'
-  min: '-1.919e+00'
-  shape:
-  - 128
-  - 10
-  sum: '8.586e+01'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/cifar10_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cifar10_jax_cnn_jax_image_classifier.yaml
similarity index 52%
rename from .regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/cifar10_jax_cnn_jax_image_classifier.yaml
rename to .regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cifar10_jax_cnn_jax_image_classifier.yaml
index 08aaae50..5f76c79f 100644
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/cifar10_jax_cnn_jax_image_classifier.yaml
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cifar10_jax_cnn_jax_image_classifier.yaml
@@ -1,13 +1,13 @@
 network.params.0:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
+  device: cpu
+  max: '1.095e-05'
+  mean: '-1.787e-06'
+  min: '-1.033e-05'
   shape:
   - 32
-  sum: '0.e+00'
+  sum: '-5.719e-05'
 network.params.1:
-  device: cuda:0
+  device: cpu
   max: '4.299e-01'
   mean: '-8.263e-03'
   min: '-4.351e-01'
@@ -18,51 +18,51 @@ network.params.1:
   - 32
   sum: '-7.139e+00'
 network.params.2:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
+  device: cpu
+  max: '8.109e-06'
+  mean: '-1.604e-06'
+  min: '-1.36e-05'
   shape:
   - 64
-  sum: '0.e+00'
+  sum: '-1.026e-04'
 network.params.3:
-  device: cuda:0
+  device: cpu
   max: '1.337e-01'
-  mean: '4.516e-04'
+  mean: '4.511e-04'
   min: '-1.34e-01'
   shape:
   - 3
   - 3
   - 32
   - 64
-  sum: '8.325e+00'
+  sum: '8.315e+00'
 network.params.4:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
+  device: cpu
+  max: '8.475e-06'
+  mean: '-3.320e-07'
+  min: '-9.955e-06'
   shape:
   - 256
-  sum: '0.e+00'
+  sum: '-8.5e-05'
 network.params.5:
-  device: cuda:0
+  device: cpu
   max: '3.553e-02'
-  mean: '1.659e-05'
+  mean: '1.650e-05'
   min: '-3.553e-02'
   shape:
   - 4096
   - 256
-  sum: '1.739e+01'
+  sum: '1.731e+01'
 network.params.6:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
+  device: cpu
+  max: '2.593e-05'
+  mean: '3.638e-13'
+  min: '-3.249e-05'
   shape:
   - 10
-  sum: '0.e+00'
+  sum: '3.638e-12'
 network.params.7:
-  device: cuda:0
+  device: cpu
   max: '1.421e-01'
   mean: '7.197e-04'
   min: '-1.416e-01'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/cifar10_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cifar10_jax_fcnet_jax_image_classifier.yaml
similarity index 51%
rename from .regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/cifar10_jax_fcnet_jax_image_classifier.yaml
rename to .regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cifar10_jax_fcnet_jax_image_classifier.yaml
index 178d3b7e..a49a4abf 100644
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/cifar10_jax_fcnet_jax_image_classifier.yaml
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cifar10_jax_fcnet_jax_image_classifier.yaml
@@ -1,30 +1,30 @@
 network.params.0:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
+  device: cpu
+  max: '1.13e-05'
+  mean: '-6.641e-07'
+  min: '-1.519e-05'
   shape:
   - 256
-  sum: '0.e+00'
+  sum: '-1.700e-04'
 network.params.1:
-  device: cuda:0
-  max: '4.102e-02'
-  mean: '2.969e-05'
-  min: '-4.102e-02'
+  device: cpu
+  max: '4.103e-02'
+  mean: '2.964e-05'
+  min: '-4.103e-02'
   shape:
   - 3072
   - 256
-  sum: '2.335e+01'
+  sum: '2.331e+01'
 network.params.2:
-  device: cuda:0
-  max: '0.e+00'
+  device: cpu
+  max: '3.123e-05'
   mean: '0.e+00'
-  min: '0.e+00'
+  min: '-6.439e-05'
   shape:
   - 10
   sum: '0.e+00'
 network.params.3:
-  device: cuda:0
+  device: cpu
   max: '1.421e-01'
   mean: '7.197e-04'
   min: '-1.416e-01'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/fashion_mnist_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/fashion_mnist_jax_cnn_jax_image_classifier.yaml
deleted file mode 100644
index 12deaed2..00000000
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/fashion_mnist_jax_cnn_jax_image_classifier.yaml
+++ /dev/null
@@ -1,72 +0,0 @@
-network.params.0:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 32
-  sum: '0.e+00'
-network.params.1:
-  device: cuda:0
-  max: '7.276e-01'
-  mean: '-9.743e-04'
-  min: '-7.453e-01'
-  shape:
-  - 3
-  - 3
-  - 1
-  - 32
-  sum: '-2.806e-01'
-network.params.2:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.params.3:
-  device: cuda:0
-  max: '1.337e-01'
-  mean: '4.516e-04'
-  min: '-1.34e-01'
-  shape:
-  - 3
-  - 3
-  - 32
-  - 64
-  sum: '8.325e+00'
-network.params.4:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.params.5:
-  device: cuda:0
-  max: '4.060e-02'
-  mean: '1.956e-05'
-  min: '-4.060e-02'
-  shape:
-  - 3136
-  - 256
-  sum: '1.570e+01'
-network.params.6:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 10
-  sum: '0.e+00'
-network.params.7:
-  device: cuda:0
-  max: '1.421e-01'
-  mean: '7.197e-04'
-  min: '-1.416e-01'
-  shape:
-  - 256
-  - 10
-  sum: '1.842e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/fashion_mnist_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/fashion_mnist_jax_fcnet_jax_image_classifier.yaml
deleted file mode 100644
index b29367ad..00000000
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/fashion_mnist_jax_fcnet_jax_image_classifier.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-network.params.0:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.params.1:
-  device: cuda:0
-  max: '8.120e-02'
-  mean: '-2.572e-05'
-  min: '-8.120e-02'
-  shape:
-  - 784
-  - 256
-  sum: '-5.162e+00'
-network.params.2:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 10
-  sum: '0.e+00'
-network.params.3:
-  device: cuda:0
-  max: '1.421e-01'
-  mean: '7.197e-04'
-  min: '-1.416e-01'
-  shape:
-  - 256
-  - 10
-  sum: '1.842e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/mnist_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/mnist_jax_cnn_jax_image_classifier.yaml
deleted file mode 100644
index 12deaed2..00000000
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/mnist_jax_cnn_jax_image_classifier.yaml
+++ /dev/null
@@ -1,72 +0,0 @@
-network.params.0:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 32
-  sum: '0.e+00'
-network.params.1:
-  device: cuda:0
-  max: '7.276e-01'
-  mean: '-9.743e-04'
-  min: '-7.453e-01'
-  shape:
-  - 3
-  - 3
-  - 1
-  - 32
-  sum: '-2.806e-01'
-network.params.2:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 64
-  sum: '0.e+00'
-network.params.3:
-  device: cuda:0
-  max: '1.337e-01'
-  mean: '4.516e-04'
-  min: '-1.34e-01'
-  shape:
-  - 3
-  - 3
-  - 32
-  - 64
-  sum: '8.325e+00'
-network.params.4:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 256
-  sum: '0.e+00'
-network.params.5:
-  device: cuda:0
-  max: '4.060e-02'
-  mean: '1.956e-05'
-  min: '-4.060e-02'
-  shape:
-  - 3136
-  - 256
-  sum: '1.570e+01'
-network.params.6:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
-  shape:
-  - 10
-  sum: '0.e+00'
-network.params.7:
-  device: cuda:0
-  max: '1.421e-01'
-  mean: '7.197e-04'
-  min: '-1.416e-01'
-  shape:
-  - 256
-  - 10
-  sum: '1.842e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/fashion_mnist_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/fashion_mnist_jax_cnn_jax_image_classifier.yaml
new file mode 100644
index 00000000..4ec020b1
--- /dev/null
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/fashion_mnist_jax_cnn_jax_image_classifier.yaml
@@ -0,0 +1,72 @@
+network.params.0:
+  device: cpu
+  max: '1.937e-05'
+  mean: '-3.894e-06'
+  min: '-1.939e-05'
+  shape:
+  - 32
+  sum: '-1.246e-04'
+network.params.1:
+  device: cpu
+  max: '7.276e-01'
+  mean: '-9.797e-04'
+  min: '-7.453e-01'
+  shape:
+  - 3
+  - 3
+  - 1
+  - 32
+  sum: '-2.821e-01'
+network.params.2:
+  device: cpu
+  max: '1.461e-05'
+  mean: '-2.084e-06'
+  min: '-2.629e-05'
+  shape:
+  - 64
+  sum: '-1.334e-04'
+network.params.3:
+  device: cpu
+  max: '1.337e-01'
+  mean: '4.502e-04'
+  min: '-1.34e-01'
+  shape:
+  - 3
+  - 3
+  - 32
+  - 64
+  sum: '8.298e+00'
+network.params.4:
+  device: cpu
+  max: '1.772e-05'
+  mean: '-1.059e-06'
+  min: '-2.387e-05'
+  shape:
+  - 256
+  sum: '-2.711e-04'
+network.params.5:
+  device: cpu
+  max: '4.060e-02'
+  mean: '1.924e-05'
+  min: '-4.060e-02'
+  shape:
+  - 3136
+  - 256
+  sum: '1.545e+01'
+network.params.6:
+  device: cpu
+  max: '6.966e-05'
+  mean: '-5.457e-13'
+  min: '-6.150e-05'
+  shape:
+  - 10
+  sum: '-5.457e-12'
+network.params.7:
+  device: cpu
+  max: '1.421e-01'
+  mean: '7.197e-04'
+  min: '-1.416e-01'
+  shape:
+  - 256
+  - 10
+  sum: '1.842e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/fashion_mnist_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/fashion_mnist_jax_fcnet_jax_image_classifier.yaml
new file mode 100644
index 00000000..11f8982d
--- /dev/null
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/fashion_mnist_jax_fcnet_jax_image_classifier.yaml
@@ -0,0 +1,34 @@
+network.params.0:
+  device: cpu
+  max: '1.89e-05'
+  mean: '-6.964e-07'
+  min: '-2.169e-05'
+  shape:
+  - 256
+  sum: '-1.783e-04'
+network.params.1:
+  device: cpu
+  max: '8.120e-02'
+  mean: '-2.607e-05'
+  min: '-8.121e-02'
+  shape:
+  - 784
+  - 256
+  sum: '-5.232e+00'
+network.params.2:
+  device: cpu
+  max: '9.016e-05'
+  mean: '1.091e-12'
+  min: '-1.382e-04'
+  shape:
+  - 10
+  sum: '1.091e-11'
+network.params.3:
+  device: cpu
+  max: '1.421e-01'
+  mean: '7.197e-04'
+  min: '-1.416e-01'
+  shape:
+  - 256
+  - 10
+  sum: '1.842e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/mnist_jax_cnn_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/mnist_jax_cnn_jax_image_classifier.yaml
new file mode 100644
index 00000000..22cc8e47
--- /dev/null
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/mnist_jax_cnn_jax_image_classifier.yaml
@@ -0,0 +1,72 @@
+network.params.0:
+  device: cpu
+  max: '9.400e-06'
+  mean: '-1.768e-06'
+  min: '-1.631e-05'
+  shape:
+  - 32
+  sum: '-5.657e-05'
+network.params.1:
+  device: cpu
+  max: '7.276e-01'
+  mean: '-9.759e-04'
+  min: '-7.453e-01'
+  shape:
+  - 3
+  - 3
+  - 1
+  - 32
+  sum: '-2.810e-01'
+network.params.2:
+  device: cpu
+  max: '1.943e-05'
+  mean: '-1.279e-06'
+  min: '-1.839e-05'
+  shape:
+  - 64
+  sum: '-8.189e-05'
+network.params.3:
+  device: cpu
+  max: '1.337e-01'
+  mean: '4.508e-04'
+  min: '-1.34e-01'
+  shape:
+  - 3
+  - 3
+  - 32
+  - 64
+  sum: '8.31e+00'
+network.params.4:
+  device: cpu
+  max: '1.649e-05'
+  mean: '-4.503e-07'
+  min: '-2.015e-05'
+  shape:
+  - 256
+  sum: '-1.153e-04'
+network.params.5:
+  device: cpu
+  max: '4.060e-02'
+  mean: '1.948e-05'
+  min: '-4.060e-02'
+  shape:
+  - 3136
+  - 256
+  sum: '1.564e+01'
+network.params.6:
+  device: cpu
+  max: '7.932e-05'
+  mean: '1.16e-12'
+  min: '-6.867e-05'
+  shape:
+  - 10
+  sum: '1.16e-11'
+network.params.7:
+  device: cpu
+  max: '1.421e-01'
+  mean: '7.197e-04'
+  min: '-1.416e-01'
+  shape:
+  - 256
+  - 10
+  sum: '1.842e+00'
diff --git a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/mnist_jax_fcnet_jax_image_classifier.yaml b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/mnist_jax_fcnet_jax_image_classifier.yaml
similarity index 51%
rename from .regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/mnist_jax_fcnet_jax_image_classifier.yaml
rename to .regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/mnist_jax_fcnet_jax_image_classifier.yaml
index b29367ad..6253169c 100644
--- a/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/cuda/mnist_jax_fcnet_jax_image_classifier.yaml
+++ b/.regression_files/project/algorithms/jax_image_classifier_test/test_initialization_is_reproducible/mnist_jax_fcnet_jax_image_classifier.yaml
@@ -1,30 +1,30 @@
 network.params.0:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
+  device: cpu
+  max: '1.135e-05'
+  mean: '-7.16e-07'
+  min: '-1.272e-05'
   shape:
   - 256
-  sum: '0.e+00'
+  sum: '-1.833e-04'
 network.params.1:
-  device: cuda:0
+  device: cpu
   max: '8.120e-02'
-  mean: '-2.572e-05'
+  mean: '-2.561e-05'
   min: '-8.120e-02'
   shape:
   - 784
   - 256
-  sum: '-5.162e+00'
+  sum: '-5.141e+00'
 network.params.2:
-  device: cuda:0
-  max: '0.e+00'
-  mean: '0.e+00'
-  min: '0.e+00'
+  device: cpu
+  max: '7.950e-05'
+  mean: '-1.054e-12'
+  min: '-4.535e-05'
   shape:
   - 10
-  sum: '0.e+00'
+  sum: '-1.054e-11'
 network.params.3:
-  device: cuda:0
+  device: cpu
   max: '1.421e-01'
   mean: '7.197e-04'
   min: '-1.416e-01'
diff --git a/.regression_files/project/algorithms/llm_finetuning_test/test_forward_pass_is_reproducible/cuda/llm_finetuning.yaml b/.regression_files/project/algorithms/llm_finetuning_test/test_forward_pass_is_reproducible/cpu/llm_finetuning.yaml
similarity index 95%
rename from .regression_files/project/algorithms/llm_finetuning_test/test_forward_pass_is_reproducible/cuda/llm_finetuning.yaml
rename to .regression_files/project/algorithms/llm_finetuning_test/test_forward_pass_is_reproducible/cpu/llm_finetuning.yaml
index 41f33102..99f8a908 100644
--- a/.regression_files/project/algorithms/llm_finetuning_test/test_forward_pass_is_reproducible/cuda/llm_finetuning.yaml
+++ b/.regression_files/project/algorithms/llm_finetuning_test/test_forward_pass_is_reproducible/cpu/llm_finetuning.yaml
@@ -1,30 +1,3 @@
-input.attention_mask:
-  device: cuda:0
-  max: 1
-  mean: '1.e+00'
-  min: 1
-  shape:
-  - 8
-  - 256
-  sum: 2048
-input.input_ids:
-  device: cuda:0
-  max: 50118
-  mean: '5.447e+03'
-  min: 2
-  shape:
-  - 8
-  - 256
-  sum: 11154886
-input.labels:
-  device: cuda:0
-  max: 50118
-  mean: '5.447e+03'
-  min: 2
-  shape:
-  - 8
-  - 256
-  sum: 11154886
 out.logits:
   device: cuda:0
   max: '3.537e+01'
diff --git a/.regression_files/project/algorithms/llm_finetuning_test/test_initialization_is_reproducible/cuda/llm_finetuning.yaml b/.regression_files/project/algorithms/llm_finetuning_test/test_initialization_is_reproducible/llm_finetuning.yaml
similarity index 66%
rename from .regression_files/project/algorithms/llm_finetuning_test/test_initialization_is_reproducible/cuda/llm_finetuning.yaml
rename to .regression_files/project/algorithms/llm_finetuning_test/test_initialization_is_reproducible/llm_finetuning.yaml
index 9e7c6ffb..0ccba294 100644
--- a/.regression_files/project/algorithms/llm_finetuning_test/test_initialization_is_reproducible/cuda/llm_finetuning.yaml
+++ b/.regression_files/project/algorithms/llm_finetuning_test/test_initialization_is_reproducible/llm_finetuning.yaml
@@ -1,14 +1,14 @@
 network.lm_head.weight:
-  device: cuda:0
+  device: cpu
   max: '2.372e-01'
   mean: '-1.208e-03'
-  min: '-2.5e-01'
+  min: '-2.500e-01'
   shape:
   - 50272
   - 512
-  sum: '-3.109e+04'
+  sum: '-3.110e+04'
 network.model.decoder.embed_positions.weight:
-  device: cuda:0
+  device: cpu
   max: '1.327e-01'
   mean: '1.768e-05'
   min: '-1.379e-01'
@@ -17,25 +17,25 @@ network.model.decoder.embed_positions.weight:
   - 1024
   sum: '3.711e+01'
 network.model.decoder.embed_tokens.weight:
-  device: cuda:0
+  device: cpu
   max: '2.372e-01'
   mean: '-1.208e-03'
-  min: '-2.5e-01'
+  min: '-2.500e-01'
   shape:
   - 50272
   - 512
-  sum: '-3.109e+04'
+  sum: '-3.110e+04'
 network.model.decoder.layers.0.fc1.bias:
-  device: cuda:0
-  max: '1.249e-01'
+  device: cpu
+  max: '1.25e-01'
   mean: '-2.961e-02'
   min: '-1.085e-01'
   shape:
   - 4096
   sum: '-1.213e+02'
 network.model.decoder.layers.0.fc1.weight:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '1.667e-04'
   min: '-1.251e-01'
   shape:
@@ -43,24 +43,24 @@ network.model.decoder.layers.0.fc1.weight:
   - 1024
   sum: '6.992e+02'
 network.model.decoder.layers.0.fc2.bias:
-  device: cuda:0
-  max: '7.88e-02'
-  mean: '-8.293e-05'
-  min: '-9.351e-02'
+  device: cpu
+  max: '7.882e-02'
+  mean: '-8.273e-05'
+  min: '-9.353e-02'
   shape:
   - 1024
-  sum: '-8.492e-02'
+  sum: '-8.472e-02'
 network.model.decoder.layers.0.fc2.weight:
-  device: cuda:0
-  max: '1.331e-01'
-  mean: '5.357e-06'
+  device: cpu
+  max: '1.330e-01'
+  mean: '5.366e-06'
   min: '-1.448e-01'
   shape:
   - 1024
   - 4096
-  sum: '2.247e+01'
+  sum: '2.251e+01'
 network.model.decoder.layers.0.final_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.256e-01'
   mean: '7.015e-03'
   min: '-1.204e-01'
@@ -68,15 +68,15 @@ network.model.decoder.layers.0.final_layer_norm.bias:
   - 1024
   sum: '7.183e+00'
 network.model.decoder.layers.0.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.0.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '3.125e-02'
   mean: '3.414e-04'
   min: '-3.123e-02'
@@ -84,92 +84,92 @@ network.model.decoder.layers.0.self_attn.k_proj.bias:
   - 1024
   sum: '3.496e-01'
 network.model.decoder.layers.0.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.256e-01'
-  mean: '-4.626e-05'
+  mean: '-4.627e-05'
   min: '-1.256e-01'
   shape:
   - 1024
   - 1024
-  sum: '-4.850e+01'
+  sum: '-4.852e+01'
 network.model.decoder.layers.0.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '1.579e-02'
-  mean: '-2.766e-05'
-  min: '-1.138e-02'
+  device: cpu
+  max: '1.581e-02'
+  mean: '-2.759e-05'
+  min: '-1.140e-02'
   shape:
   - 1024
-  sum: '-2.833e-02'
+  sum: '-2.825e-02'
 network.model.decoder.layers.0.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.283e-01'
-  mean: '-6.181e-06'
+  mean: '-6.18e-06'
   min: '-1.295e-01'
   shape:
   - 1024
   - 1024
-  sum: '-6.481e+00'
+  sum: '-6.480e+00'
 network.model.decoder.layers.0.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.282e-01'
   mean: '1.180e-03'
   min: '-1.271e-01'
   shape:
   - 1024
-  sum: '1.208e+00'
+  sum: '1.209e+00'
 network.model.decoder.layers.0.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.267e-01'
-  mean: '-5.663e-05'
+  mean: '-5.664e-05'
   min: '-1.267e-01'
   shape:
   - 1024
   - 1024
-  sum: '-5.938e+01'
+  sum: '-5.939e+01'
 network.model.decoder.layers.0.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '2.769e-02'
-  mean: '-2.715e-05'
-  min: '-2.669e-02'
+  device: cpu
+  max: '2.771e-02'
+  mean: '-2.707e-05'
+  min: '-2.667e-02'
   shape:
   - 1024
-  sum: '-2.780e-02'
+  sum: '-2.772e-02'
 network.model.decoder.layers.0.self_attn.v_proj.weight:
-  device: cuda:0
-  max: '8.795e-02'
-  mean: '1.917e-06'
-  min: '-8.508e-02'
+  device: cpu
+  max: '8.797e-02'
+  mean: '1.945e-06'
+  min: '-8.506e-02'
   shape:
   - 1024
   - 1024
-  sum: '2.011e+00'
+  sum: '2.04e+00'
 network.model.decoder.layers.0.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.271e-01'
-  mean: '-2.03e-03'
+  mean: '-2.029e-03'
   min: '-1.248e-01'
   shape:
   - 1024
-  sum: '-2.079e+00'
+  sum: '-2.078e+00'
 network.model.decoder.layers.0.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.1.fc1.bias:
-  device: cuda:0
+  device: cpu
   max: '1.236e-01'
   mean: '-2.428e-02'
-  min: '-8.075e-02'
+  min: '-8.073e-02'
   shape:
   - 4096
   sum: '-9.946e+01'
 network.model.decoder.layers.1.fc1.weight:
-  device: cuda:0
-  max: '1.254e-01'
+  device: cpu
+  max: '1.253e-01'
   mean: '1.85e-04'
   min: '-1.261e-01'
   shape:
@@ -177,40 +177,40 @@ network.model.decoder.layers.1.fc1.weight:
   - 1024
   sum: '7.759e+02'
 network.model.decoder.layers.1.fc2.bias:
-  device: cuda:0
-  max: '8.911e-02'
-  mean: '2.946e-04'
-  min: '-8.362e-02'
+  device: cpu
+  max: '8.913e-02'
+  mean: '2.952e-04'
+  min: '-8.364e-02'
   shape:
   - 1024
-  sum: '3.017e-01'
+  sum: '3.023e-01'
 network.model.decoder.layers.1.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.321e-01'
-  mean: '-2.468e-06'
+  mean: '-2.469e-06'
   min: '-2.5e-01'
   shape:
   - 1024
   - 4096
   sum: '-1.035e+01'
 network.model.decoder.layers.1.final_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.256e-01'
-  mean: '8.647e-03'
+  mean: '8.648e-03'
   min: '-1.198e-01'
   shape:
   - 1024
-  sum: '8.855e+00'
+  sum: '8.856e+00'
 network.model.decoder.layers.1.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.1.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '7.153e-02'
   mean: '7.902e-03'
   min: '-7.874e-02'
@@ -218,91 +218,91 @@ network.model.decoder.layers.1.self_attn.k_proj.bias:
   - 1024
   sum: '8.092e+00'
 network.model.decoder.layers.1.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.266e-01'
-  mean: '-1.284e-05'
+  mean: '-1.283e-05'
   min: '-1.272e-01'
   shape:
   - 1024
   - 1024
   sum: '-1.346e+01'
 network.model.decoder.layers.1.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '8.606e-02'
-  mean: '-1.118e-04'
-  min: '-7.031e-02'
+  device: cpu
+  max: '8.608e-02'
+  mean: '-1.113e-04'
+  min: '-7.029e-02'
   shape:
   - 1024
-  sum: '-1.144e-01'
+  sum: '-1.14e-01'
 network.model.decoder.layers.1.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.266e-01'
-  mean: '1.676e-06'
+  mean: '1.672e-06'
   min: '-1.272e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.758e+00'
+  sum: '1.753e+00'
 network.model.decoder.layers.1.self_attn.q_proj.bias:
-  device: cuda:0
-  max: '1.254e-01'
-  mean: '-1.557e-03'
+  device: cpu
+  max: '1.253e-01'
+  mean: '-1.558e-03'
   min: '-1.252e-01'
   shape:
   - 1024
   sum: '-1.595e+00'
 network.model.decoder.layers.1.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.256e-01'
-  mean: '-3.561e-05'
+  mean: '-3.563e-05'
   min: '-1.26e-01'
   shape:
   - 1024
   - 1024
-  sum: '-3.734e+01'
+  sum: '-3.736e+01'
 network.model.decoder.layers.1.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '5.002e-02'
-  mean: '3.967e-04'
-  min: '-4.831e-02'
+  device: cpu
+  max: '5.e-02'
+  mean: '3.956e-04'
+  min: '-4.833e-02'
   shape:
   - 1024
-  sum: '4.062e-01'
+  sum: '4.051e-01'
 network.model.decoder.layers.1.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.092e-01'
-  mean: '1.417e-05'
+  mean: '1.420e-05'
   min: '-1.07e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.486e+01'
+  sum: '1.489e+01'
 network.model.decoder.layers.1.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.304e-01'
   mean: '-2.029e-03'
   min: '-1.248e-01'
   shape:
   - 1024
-  sum: '-2.078e+00'
+  sum: '-2.077e+00'
 network.model.decoder.layers.1.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.10.fc1.bias:
-  device: cuda:0
-  max: '5.505e-02'
+  device: cpu
+  max: '5.507e-02'
   mean: '-2.099e-02'
-  min: '-8.49e-02'
+  min: '-8.488e-02'
   shape:
   - 4096
   sum: '-8.599e+01'
 network.model.decoder.layers.10.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.27e-01'
   mean: '1.603e-05'
   min: '-1.296e-01'
@@ -311,40 +311,40 @@ network.model.decoder.layers.10.fc1.weight:
   - 1024
   sum: '6.723e+01'
 network.model.decoder.layers.10.fc2.bias:
-  device: cuda:0
-  max: '6.293e-02'
-  mean: '-1.937e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '6.295e-02'
+  mean: '-1.943e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '-1.983e-01'
+  sum: '-1.99e-01'
 network.model.decoder.layers.10.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.281e-01'
-  mean: '-1.624e-06'
+  mean: '-1.623e-06'
   min: '-2.5e-01'
   shape:
   - 1024
   - 4096
-  sum: '-6.81e+00'
+  sum: '-6.806e+00'
 network.model.decoder.layers.10.final_layer_norm.bias:
-  device: cuda:0
-  max: '8.020e-02'
-  mean: '-9.374e-03'
-  min: '-1.25e-01'
+  device: cpu
+  max: '8.018e-02'
+  mean: '-9.375e-03'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '-9.599e+00'
+  sum: '-9.6e+00'
 network.model.decoder.layers.10.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.10.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '7.422e-02'
   mean: '7.871e-03'
   min: '-7.428e-02'
@@ -352,33 +352,33 @@ network.model.decoder.layers.10.self_attn.k_proj.bias:
   - 1024
   sum: '8.06e+00'
 network.model.decoder.layers.10.self_attn.k_proj.weight:
-  device: cuda:0
-  max: '1.318e-01'
-  mean: '-1.478e-05'
-  min: '-1.285e-01'
+  device: cpu
+  max: '1.319e-01'
+  mean: '-1.482e-05'
+  min: '-1.286e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.55e+01'
+  sum: '-1.554e+01'
 network.model.decoder.layers.10.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '7.031e-02'
-  mean: '-2.308e-05'
-  min: '-1.25e-01'
+  device: cpu
+  max: '7.033e-02'
+  mean: '-2.276e-05'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '-2.363e-02'
+  sum: '-2.331e-02'
 network.model.decoder.layers.10.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.321e-01'
-  mean: '1.384e-06'
+  mean: '1.382e-06'
   min: '-1.316e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.452e+00'
+  sum: '1.449e+00'
 network.model.decoder.layers.10.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.089e-01'
   mean: '-1.708e-03'
   min: '-1.009e-01'
@@ -386,99 +386,99 @@ network.model.decoder.layers.10.self_attn.q_proj.bias:
   - 1024
   sum: '-1.749e+00'
 network.model.decoder.layers.10.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.300e-01'
-  mean: '5.200e-06'
+  mean: '5.191e-06'
   min: '-1.311e-01'
   shape:
   - 1024
   - 1024
-  sum: '5.453e+00'
+  sum: '5.443e+00'
 network.model.decoder.layers.10.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '5.096e-02'
-  mean: '3.204e-04'
-  min: '-5.444e-02'
+  device: cpu
+  max: '5.094e-02'
+  mean: '3.211e-04'
+  min: '-5.442e-02'
   shape:
   - 1024
-  sum: '3.281e-01'
+  sum: '3.288e-01'
 network.model.decoder.layers.10.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.241e-01'
-  mean: '1.173e-05'
+  mean: '1.185e-05'
   min: '-1.152e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.229e+01'
+  sum: '1.243e+01'
 network.model.decoder.layers.10.self_attn_layer_norm.bias:
-  device: cuda:0
-  max: '8.594e-02'
-  mean: '1.188e-03'
-  min: '-1.25e-01'
+  device: cpu
+  max: '8.596e-02'
+  mean: '1.189e-03'
+  min: '-1.250e-01'
   shape:
   - 1024
   sum: '1.217e+00'
 network.model.decoder.layers.10.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.11.fc1.bias:
-  device: cuda:0
-  max: '6.107e-02'
+  device: cpu
+  max: '6.105e-02'
   mean: '-2.344e-02'
-  min: '-8.850e-02'
+  min: '-8.848e-02'
   shape:
   - 4096
   sum: '-9.601e+01'
 network.model.decoder.layers.11.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.257e-01'
   mean: '-1.888e-04'
-  min: '-1.263e-01'
+  min: '-1.264e-01'
   shape:
   - 4096
   - 1024
-  sum: '-7.920e+02'
+  sum: '-7.92e+02'
 network.model.decoder.layers.11.fc2.bias:
-  device: cuda:0
-  max: '6.47e-02'
-  mean: '1.148e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '6.472e-02'
+  mean: '1.142e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '1.176e-01'
+  sum: '1.169e-01'
 network.model.decoder.layers.11.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.26e-01'
-  mean: '3.113e-07'
+  mean: '2.676e-07'
   min: '-2.5e-01'
   shape:
   - 1024
   - 4096
-  sum: '1.306e+00'
+  sum: '1.123e+00'
 network.model.decoder.layers.11.final_layer_norm.bias:
-  device: cuda:0
-  max: '7.886e-02'
+  device: cpu
+  max: '7.884e-02'
   mean: '-1.455e-02'
-  min: '-1.25e-01'
+  min: '-1.250e-01'
   shape:
   - 1024
   sum: '-1.489e+01'
 network.model.decoder.layers.11.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
+  mean: '1.000e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.11.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '7.074e-02'
   mean: '5.886e-03'
   min: '-6.482e-02'
@@ -486,91 +486,91 @@ network.model.decoder.layers.11.self_attn.k_proj.bias:
   - 1024
   sum: '6.027e+00'
 network.model.decoder.layers.11.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.331e-01'
-  mean: '1.017e-05'
-  min: '-1.31e-01'
+  mean: '1.019e-05'
+  min: '-1.310e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.066e+01'
+  sum: '1.069e+01'
 network.model.decoder.layers.11.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '6.311e-02'
-  mean: '-3.316e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '6.309e-02'
+  mean: '-3.320e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '-3.396e-01'
+  sum: '-3.4e-01'
 network.model.decoder.layers.11.self_attn.out_proj.weight:
-  device: cuda:0
-  max: '1.514e-01'
-  mean: '1.601e-05'
+  device: cpu
+  max: '1.513e-01'
+  mean: '1.604e-05'
   min: '-1.647e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.679e+01'
+  sum: '1.682e+01'
 network.model.decoder.layers.11.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.105e-01'
-  mean: '-2.709e-03'
+  mean: '-2.708e-03'
   min: '-1.172e-01'
   shape:
   - 1024
-  sum: '-2.774e+00'
+  sum: '-2.773e+00'
 network.model.decoder.layers.11.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.287e-01'
-  mean: '5.092e-06'
+  mean: '5.077e-06'
   min: '-1.26e-01'
   shape:
   - 1024
   - 1024
-  sum: '5.339e+00'
+  sum: '5.324e+00'
 network.model.decoder.layers.11.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '3.922e-02'
-  mean: '4.083e-04'
-  min: '-4.712e-02'
+  device: cpu
+  max: '3.92e-02'
+  mean: '4.086e-04'
+  min: '-4.714e-02'
   shape:
   - 1024
-  sum: '4.180e-01'
+  sum: '4.184e-01'
 network.model.decoder.layers.11.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.234e-01'
-  mean: '-8.525e-05'
+  mean: '-8.513e-05'
   min: '-1.197e-01'
   shape:
   - 1024
   - 1024
-  sum: '-8.939e+01'
+  sum: '-8.926e+01'
 network.model.decoder.layers.11.self_attn_layer_norm.bias:
-  device: cuda:0
-  max: '1.046e-01'
-  mean: '4.110e-03'
-  min: '-1.25e-01'
+  device: cpu
+  max: '1.045e-01'
+  mean: '4.11e-03'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '4.209e+00'
+  sum: '4.208e+00'
 network.model.decoder.layers.11.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.12.fc1.bias:
-  device: cuda:0
-  max: '7.367e-02'
+  device: cpu
+  max: '7.365e-02'
   mean: '-2.188e-02'
-  min: '-7.434e-02'
+  min: '-7.432e-02'
   shape:
   - 4096
   sum: '-8.961e+01'
 network.model.decoder.layers.12.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.274e-01'
   mean: '-2.221e-04'
   min: '-1.266e-01'
@@ -579,40 +579,40 @@ network.model.decoder.layers.12.fc1.weight:
   - 1024
   sum: '-9.314e+02'
 network.model.decoder.layers.12.fc2.bias:
-  device: cuda:0
-  max: '7.233e-02'
-  mean: '-3.044e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '7.235e-02'
+  mean: '-3.048e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '-3.118e-01'
+  sum: '-3.122e-01'
 network.model.decoder.layers.12.fc2.weight:
-  device: cuda:0
-  max: '1.265e-01'
-  mean: '1.128e-07'
+  device: cpu
+  max: '1.264e-01'
+  mean: '6.248e-08'
   min: '-1.393e-01'
   shape:
   - 1024
   - 4096
-  sum: '4.732e-01'
+  sum: '2.621e-01'
 network.model.decoder.layers.12.final_layer_norm.bias:
-  device: cuda:0
-  max: '1.241e-01'
+  device: cpu
+  max: '1.242e-01'
   mean: '-1.53e-02'
   min: '-1.254e-01'
   shape:
   - 1024
   sum: '-1.566e+01'
 network.model.decoder.layers.12.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.12.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.177e-01'
   mean: '6.118e-03'
   min: '-8.82e-02'
@@ -620,91 +620,91 @@ network.model.decoder.layers.12.self_attn.k_proj.bias:
   - 1024
   sum: '6.265e+00'
 network.model.decoder.layers.12.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.274e-01'
-  mean: '2.051e-05'
+  mean: '2.054e-05'
   min: '-1.263e-01'
   shape:
   - 1024
   - 1024
-  sum: '2.151e+01'
+  sum: '2.154e+01'
 network.model.decoder.layers.12.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '6.604e-02'
-  mean: '-4.053e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '6.602e-02'
+  mean: '-4.060e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '-4.151e-01'
+  sum: '-4.158e-01'
 network.model.decoder.layers.12.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.273e-01'
-  mean: '6.458e-06'
-  min: '-1.268e-01'
+  mean: '6.467e-06'
+  min: '-1.269e-01'
   shape:
   - 1024
   - 1024
-  sum: '6.772e+00'
+  sum: '6.781e+00'
 network.model.decoder.layers.12.self_attn.q_proj.bias:
-  device: cuda:0
-  max: '1.249e-01'
-  mean: '3.377e-04'
+  device: cpu
+  max: '1.25e-01'
+  mean: '3.374e-04'
   min: '-1.248e-01'
   shape:
   - 1024
-  sum: '3.458e-01'
+  sum: '3.455e-01'
 network.model.decoder.layers.12.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.262e-01'
-  mean: '-4.44e-05'
+  mean: '-4.439e-05'
   min: '-1.266e-01'
   shape:
   - 1024
   - 1024
-  sum: '-4.655e+01'
+  sum: '-4.654e+01'
 network.model.decoder.layers.12.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '5.71e-02'
-  mean: '1.127e-04'
-  min: '-4.361e-02'
+  device: cpu
+  max: '5.708e-02'
+  mean: '1.128e-04'
+  min: '-4.363e-02'
   shape:
   - 1024
   sum: '1.155e-01'
 network.model.decoder.layers.12.self_attn.v_proj.weight:
-  device: cuda:0
-  max: '1.246e-01'
-  mean: '5.265e-05'
+  device: cpu
+  max: '1.247e-01'
+  mean: '5.264e-05'
   min: '-1.251e-01'
   shape:
   - 1024
   - 1024
-  sum: '5.521e+01'
+  sum: '5.52e+01'
 network.model.decoder.layers.12.self_attn_layer_norm.bias:
-  device: cuda:0
-  max: '1.025e-01'
+  device: cpu
+  max: '1.026e-01'
   mean: '4.391e-03'
-  min: '-1.25e-01'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '4.497e+00'
+  sum: '4.496e+00'
 network.model.decoder.layers.12.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.13.fc1.bias:
-  device: cuda:0
-  max: '9.039e-02'
+  device: cpu
+  max: '9.037e-02'
   mean: '-2.392e-02'
-  min: '-7.361e-02'
+  min: '-7.359e-02'
   shape:
   - 4096
   sum: '-9.798e+01'
 network.model.decoder.layers.13.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.263e-01'
   mean: '-2.766e-04'
   min: '-1.261e-01'
@@ -713,24 +713,24 @@ network.model.decoder.layers.13.fc1.weight:
   - 1024
   sum: '-1.160e+03'
 network.model.decoder.layers.13.fc2.bias:
-  device: cuda:0
-  max: '7.214e-02'
-  mean: '2.524e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '7.216e-02'
+  mean: '2.522e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '2.584e-01'
+  sum: '2.582e-01'
 network.model.decoder.layers.13.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.256e-01'
-  mean: '-2.636e-06'
+  mean: '-2.719e-06'
   min: '-1.754e-01'
   shape:
   - 1024
   - 4096
-  sum: '-1.106e+01'
+  sum: '-1.140e+01'
 network.model.decoder.layers.13.final_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.246e-01'
   mean: '-2.340e-02'
   min: '-1.254e-01'
@@ -738,15 +738,15 @@ network.model.decoder.layers.13.final_layer_norm.bias:
   - 1024
   sum: '-2.396e+01'
 network.model.decoder.layers.13.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.13.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '7.465e-02'
   mean: '5.789e-03'
   min: '-7.758e-02'
@@ -754,91 +754,91 @@ network.model.decoder.layers.13.self_attn.k_proj.bias:
   - 1024
   sum: '5.928e+00'
 network.model.decoder.layers.13.self_attn.k_proj.weight:
-  device: cuda:0
-  max: '1.281e-01'
-  mean: '3.542e-05'
+  device: cpu
+  max: '1.280e-01'
+  mean: '3.544e-05'
   min: '-1.283e-01'
   shape:
   - 1024
   - 1024
-  sum: '3.714e+01'
+  sum: '3.717e+01'
 network.model.decoder.layers.13.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '6.506e-02'
-  mean: '-2.055e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '6.504e-02'
+  mean: '-2.050e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '-2.104e-01'
+  sum: '-2.099e-01'
 network.model.decoder.layers.13.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.277e-01'
-  mean: '-1.117e-05'
-  min: '-1.268e-01'
+  mean: '-1.118e-05'
+  min: '-1.269e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.171e+01'
+  sum: '-1.173e+01'
 network.model.decoder.layers.13.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.247e-01'
   mean: '-2.867e-03'
-  min: '-1.138e-01'
+  min: '-1.139e-01'
   shape:
   - 1024
   sum: '-2.936e+00'
 network.model.decoder.layers.13.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.265e-01'
-  mean: '3.923e-05'
+  mean: '3.922e-05'
   min: '-1.273e-01'
   shape:
   - 1024
   - 1024
-  sum: '4.114e+01'
+  sum: '4.113e+01'
 network.model.decoder.layers.13.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.150e-02'
-  mean: '-2.426e-04'
-  min: '-4.178e-02'
+  device: cpu
+  max: '4.152e-02'
+  mean: '-2.417e-04'
+  min: '-4.176e-02'
   shape:
   - 1024
-  sum: '-2.485e-01'
+  sum: '-2.475e-01'
 network.model.decoder.layers.13.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.262e-01'
-  mean: '-6.461e-05'
+  mean: '-6.458e-05'
   min: '-1.251e-01'
   shape:
   - 1024
   - 1024
-  sum: '-6.775e+01'
+  sum: '-6.771e+01'
 network.model.decoder.layers.13.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.247e-01'
-  mean: '3.063e-03'
-  min: '-1.25e-01'
+  mean: '3.064e-03'
+  min: '-1.250e-01'
   shape:
   - 1024
   sum: '3.137e+00'
 network.model.decoder.layers.13.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.14.fc1.bias:
-  device: cuda:0
-  max: '6.329e-02'
+  device: cpu
+  max: '6.327e-02'
   mean: '-2.279e-02'
-  min: '-6.866e-02'
+  min: '-6.864e-02'
   shape:
   - 4096
   sum: '-9.333e+01'
 network.model.decoder.layers.14.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.261e-01'
   mean: '-1.687e-04'
   min: '-1.256e-01'
@@ -847,24 +847,24 @@ network.model.decoder.layers.14.fc1.weight:
   - 1024
   sum: '-7.075e+02'
 network.model.decoder.layers.14.fc2.bias:
-  device: cuda:0
-  max: '8.209e-02'
-  mean: '2.395e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '8.211e-02'
+  mean: '2.393e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '2.453e-01'
+  sum: '2.451e-01'
 network.model.decoder.layers.14.fc2.weight:
-  device: cuda:0
-  max: '1.265e-01'
-  mean: '-1.073e-06'
-  min: '-2.5e-01'
+  device: cpu
+  max: '1.264e-01'
+  mean: '-1.143e-06'
+  min: '-2.500e-01'
   shape:
   - 1024
   - 4096
-  sum: '-4.501e+00'
+  sum: '-4.793e+00'
 network.model.decoder.layers.14.final_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.249e-01'
   mean: '-2.171e-02'
   min: '-1.277e-01'
@@ -872,41 +872,41 @@ network.model.decoder.layers.14.final_layer_norm.bias:
   - 1024
   sum: '-2.223e+01'
 network.model.decoder.layers.14.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.14.self_attn.k_proj.bias:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '4.583e-03'
   min: '-1.03e-01'
   shape:
   - 1024
   sum: '4.693e+00'
 network.model.decoder.layers.14.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.265e-01'
-  mean: '3.023e-05'
+  mean: '3.024e-05'
   min: '-1.266e-01'
   shape:
   - 1024
   - 1024
-  sum: '3.170e+01'
+  sum: '3.171e+01'
 network.model.decoder.layers.14.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '6.335e-02'
-  mean: '-2.293e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '6.333e-02'
+  mean: '-2.296e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '-2.348e-01'
+  sum: '-2.351e-01'
 network.model.decoder.layers.14.self_attn.out_proj.weight:
-  device: cuda:0
-  max: '1.292e-01'
+  device: cpu
+  max: '1.291e-01'
   mean: '-1.601e-05'
   min: '-1.316e-01'
   shape:
@@ -914,91 +914,91 @@ network.model.decoder.layers.14.self_attn.out_proj.weight:
   - 1024
   sum: '-1.679e+01'
 network.model.decoder.layers.14.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.237e-01'
-  mean: '-1.509e-03'
+  mean: '-1.508e-03'
   min: '-1.181e-01'
   shape:
   - 1024
-  sum: '-1.546e+00'
+  sum: '-1.545e+00'
 network.model.decoder.layers.14.self_attn.q_proj.weight:
-  device: cuda:0
-  max: '1.263e-01'
-  mean: '3.587e-05'
+  device: cpu
+  max: '1.264e-01'
+  mean: '3.584e-05'
   min: '-1.265e-01'
   shape:
   - 1024
   - 1024
-  sum: '3.761e+01'
+  sum: '3.758e+01'
 network.model.decoder.layers.14.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.108e-02'
-  mean: '4.279e-04'
-  min: '-3.915e-02'
+  device: cpu
+  max: '4.11e-02'
+  mean: '4.274e-04'
+  min: '-3.917e-02'
   shape:
   - 1024
-  sum: '4.381e-01'
+  sum: '4.377e-01'
 network.model.decoder.layers.14.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.249e-01'
-  mean: '6.315e-06'
+  mean: '6.264e-06'
   min: '-1.249e-01'
   shape:
   - 1024
   - 1024
-  sum: '6.622e+00'
+  sum: '6.568e+00'
 network.model.decoder.layers.14.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
-  mean: '9.48e-04'
-  min: '-1.285e-01'
+  mean: '9.472e-04'
+  min: '-1.286e-01'
   shape:
   - 1024
-  sum: '9.707e-01'
+  sum: '9.699e-01'
 network.model.decoder.layers.14.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.15.fc1.bias:
-  device: cuda:0
-  max: '6.256e-02'
+  device: cpu
+  max: '6.258e-02'
   mean: '-2.178e-02'
-  min: '-7.373e-02'
+  min: '-7.375e-02'
   shape:
   - 4096
   sum: '-8.921e+01'
 network.model.decoder.layers.15.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.262e-01'
   mean: '-2.048e-04'
-  min: '-1.274e-01'
+  min: '-1.275e-01'
   shape:
   - 4096
   - 1024
-  sum: '-8.590e+02'
+  sum: '-8.589e+02'
 network.model.decoder.layers.15.fc2.bias:
-  device: cuda:0
-  max: '7.629e-02'
-  mean: '-2.647e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '7.627e-02'
+  mean: '-2.646e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '-2.711e-01'
+  sum: '-2.71e-01'
 network.model.decoder.layers.15.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.273e-01'
-  mean: '-1.300e-06'
-  min: '-2.5e-01'
+  mean: '-1.352e-06'
+  min: '-2.500e-01'
   shape:
   - 1024
   - 4096
-  sum: '-5.454e+00'
+  sum: '-5.67e+00'
 network.model.decoder.layers.15.final_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.251e-01'
   mean: '-2.09e-02'
   min: '-1.271e-01'
@@ -1006,15 +1006,15 @@ network.model.decoder.layers.15.final_layer_norm.bias:
   - 1024
   sum: '-2.14e+01'
 network.model.decoder.layers.15.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.15.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
   mean: '5.291e-03'
   min: '-8.069e-02'
@@ -1022,7 +1022,7 @@ network.model.decoder.layers.15.self_attn.k_proj.bias:
   - 1024
   sum: '5.418e+00'
 network.model.decoder.layers.15.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.259e-01'
   mean: '3.431e-05'
   min: '-1.272e-01'
@@ -1031,24 +1031,24 @@ network.model.decoder.layers.15.self_attn.k_proj.weight:
   - 1024
   sum: '3.598e+01'
 network.model.decoder.layers.15.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '6.873e-02'
-  mean: '2.003e-05'
-  min: '-1.25e-01'
+  device: cpu
+  max: '6.875e-02'
+  mean: '2.031e-05'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '2.051e-02'
+  sum: '2.079e-02'
 network.model.decoder.layers.15.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.798e-01'
-  mean: '1.003e-06'
+  mean: '1.018e-06'
   min: '-1.726e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.052e+00'
+  sum: '1.067e+00'
 network.model.decoder.layers.15.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
   mean: '1.456e-03'
   min: '-1.242e-01'
@@ -1056,99 +1056,99 @@ network.model.decoder.layers.15.self_attn.q_proj.bias:
   - 1024
   sum: '1.491e+00'
 network.model.decoder.layers.15.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.271e-01'
-  mean: '-2.108e-05'
+  mean: '-2.106e-05'
   min: '-1.259e-01'
   shape:
   - 1024
   - 1024
-  sum: '-2.21e+01'
+  sum: '-2.209e+01'
 network.model.decoder.layers.15.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.312e-02'
-  mean: '-6.573e-04'
-  min: '-4.214e-02'
+  device: cpu
+  max: '4.310e-02'
+  mean: '-6.567e-04'
+  min: '-4.216e-02'
   shape:
   - 1024
-  sum: '-6.731e-01'
+  sum: '-6.725e-01'
 network.model.decoder.layers.15.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.246e-01'
-  mean: '-1.231e-04'
+  mean: '-1.232e-04'
   min: '-1.249e-01'
   shape:
   - 1024
   - 1024
   sum: '-1.291e+02'
 network.model.decoder.layers.15.self_attn_layer_norm.bias:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '1.033e-03'
   min: '-1.627e-01'
   shape:
   - 1024
   sum: '1.058e+00'
 network.model.decoder.layers.15.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.16.fc1.bias:
-  device: cuda:0
-  max: '1.138e-01'
+  device: cpu
+  max: '1.139e-01'
   mean: '-2.057e-02'
-  min: '-8.105e-02'
+  min: '-8.103e-02'
   shape:
   - 4096
   sum: '-8.427e+01'
 network.model.decoder.layers.16.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.261e-01'
   mean: '-1.731e-04'
-  min: '-1.263e-01'
+  min: '-1.264e-01'
   shape:
   - 4096
   - 1024
   sum: '-7.259e+02'
 network.model.decoder.layers.16.fc2.bias:
-  device: cuda:0
-  max: '7.257e-02'
-  mean: '-1.059e-04'
+  device: cpu
+  max: '7.255e-02'
+  mean: '-1.056e-04'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '-1.085e-01'
+  sum: '-1.081e-01'
 network.model.decoder.layers.16.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.387e-01'
-  mean: '-4.515e-06'
+  mean: '-4.555e-06'
   min: '-2.5e-01'
   shape:
   - 1024
   - 4096
-  sum: '-1.894e+01'
+  sum: '-1.911e+01'
 network.model.decoder.layers.16.final_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
-  mean: '-1.704e-02'
+  mean: '-1.705e-02'
   min: '-1.285e-01'
   shape:
   - 1024
-  sum: '-1.745e+01'
+  sum: '-1.746e+01'
 network.model.decoder.layers.16.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.16.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.117e-01'
   mean: '6.356e-03'
   min: '-9.009e-02'
@@ -1156,92 +1156,92 @@ network.model.decoder.layers.16.self_attn.k_proj.bias:
   - 1024
   sum: '6.508e+00'
 network.model.decoder.layers.16.self_attn.k_proj.weight:
-  device: cuda:0
-  max: '1.27e-01'
-  mean: '-1.634e-05'
+  device: cpu
+  max: '1.269e-01'
+  mean: '-1.639e-05'
   min: '-1.265e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.713e+01'
+  sum: '-1.719e+01'
 network.model.decoder.layers.16.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '8.398e-02'
-  mean: '4.806e-05'
+  device: cpu
+  max: '8.396e-02'
+  mean: '4.794e-05'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '4.921e-02'
+  sum: '4.909e-02'
 network.model.decoder.layers.16.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.553e-01'
-  mean: '-3.501e-06'
+  mean: '-3.488e-06'
   min: '-1.626e-01'
   shape:
   - 1024
   - 1024
-  sum: '-3.671e+00'
+  sum: '-3.658e+00'
 network.model.decoder.layers.16.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
-  mean: '-1.884e-04'
+  mean: '-1.879e-04'
   min: '-1.246e-01'
   shape:
   - 1024
-  sum: '-1.929e-01'
+  sum: '-1.924e-01'
 network.model.decoder.layers.16.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.261e-01'
-  mean: '2.789e-06'
+  mean: '2.781e-06'
   min: '-1.278e-01'
   shape:
   - 1024
   - 1024
-  sum: '2.924e+00'
+  sum: '2.916e+00'
 network.model.decoder.layers.16.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.462e-02'
-  mean: '-7.8e-04'
-  min: '-4.309e-02'
+  device: cpu
+  max: '4.464e-02'
+  mean: '-7.796e-04'
+  min: '-4.307e-02'
   shape:
   - 1024
-  sum: '-7.987e-01'
+  sum: '-7.983e-01'
 network.model.decoder.layers.16.self_attn.v_proj.weight:
-  device: cuda:0
-  max: '1.257e-01'
-  mean: '-9.28e-05'
+  device: cpu
+  max: '1.258e-01'
+  mean: '-9.277e-05'
   min: '-1.259e-01'
   shape:
   - 1024
   - 1024
-  sum: '-9.731e+01'
+  sum: '-9.727e+01'
 network.model.decoder.layers.16.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.252e-01'
-  mean: '1.154e-03'
+  mean: '1.155e-03'
   min: '-2.112e-01'
   shape:
   - 1024
   sum: '1.182e+00'
 network.model.decoder.layers.16.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.17.fc1.bias:
-  device: cuda:0
+  device: cpu
   max: '1.113e-01'
   mean: '-2.007e-02'
-  min: '-7.483e-02'
+  min: '-7.485e-02'
   shape:
   - 4096
-  sum: '-8.219e+01'
+  sum: '-8.22e+01'
 network.model.decoder.layers.17.fc1.weight:
-  device: cuda:0
-  max: '1.27e-01'
+  device: cpu
+  max: '1.269e-01'
   mean: '-1.176e-04'
   min: '-1.266e-01'
   shape:
@@ -1249,24 +1249,24 @@ network.model.decoder.layers.17.fc1.weight:
   - 1024
   sum: '-4.934e+02'
 network.model.decoder.layers.17.fc2.bias:
-  device: cuda:0
-  max: '6.415e-02'
-  mean: '2.448e-06'
+  device: cpu
+  max: '6.417e-02'
+  mean: '2.722e-06'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '2.507e-03'
+  sum: '2.787e-03'
 network.model.decoder.layers.17.fc2.weight:
-  device: cuda:0
-  max: '1.431e-01'
-  mean: '-1.922e-06'
+  device: cpu
+  max: '1.430e-01'
+  mean: '-1.889e-06'
   min: '-2.5e-01'
   shape:
   - 1024
   - 4096
-  sum: '-8.062e+00'
+  sum: '-7.924e+00'
 network.model.decoder.layers.17.final_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
   mean: '-1.363e-02'
   min: '-1.307e-01'
@@ -1274,107 +1274,107 @@ network.model.decoder.layers.17.final_layer_norm.bias:
   - 1024
   sum: '-1.396e+01'
 network.model.decoder.layers.17.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.17.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
   mean: '3.524e-03'
-  min: '-1.25e-01'
+  min: '-1.250e-01'
   shape:
   - 1024
   sum: '3.609e+00'
 network.model.decoder.layers.17.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.257e-01'
-  mean: '-6.266e-06'
+  mean: '-6.253e-06'
   min: '-1.268e-01'
   shape:
   - 1024
   - 1024
-  sum: '-6.571e+00'
+  sum: '-6.556e+00'
 network.model.decoder.layers.17.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '8.557e-02'
-  mean: '7.932e-05'
+  device: cpu
+  max: '8.555e-02'
+  mean: '8.026e-05'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '8.123e-02'
+  sum: '8.219e-02'
 network.model.decoder.layers.17.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.682e-01'
-  mean: '1.080e-05'
-  min: '-1.591e-01'
+  mean: '1.082e-05'
+  min: '-1.590e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.133e+01'
+  sum: '1.134e+01'
 network.model.decoder.layers.17.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.081e-01'
-  mean: '8.627e-04'
+  mean: '8.628e-04'
   min: '-1.006e-01'
   shape:
   - 1024
-  sum: '8.834e-01'
+  sum: '8.835e-01'
 network.model.decoder.layers.17.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.265e-01'
-  mean: '-1.448e-05'
+  mean: '-1.446e-05'
   min: '-1.262e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.518e+01'
+  sum: '-1.517e+01'
 network.model.decoder.layers.17.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.285e-02'
-  mean: '4.112e-04'
-  min: '-4.175e-02'
+  device: cpu
+  max: '4.283e-02'
+  mean: '4.105e-04'
+  min: '-4.173e-02'
   shape:
   - 1024
-  sum: '4.211e-01'
+  sum: '4.204e-01'
 network.model.decoder.layers.17.self_attn.v_proj.weight:
-  device: cuda:0
-  max: '1.254e-01'
-  mean: '-1.06e-05'
-  min: '-1.25e-01'
+  device: cpu
+  max: '1.253e-01'
+  mean: '-1.071e-05'
+  min: '-1.250e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.111e+01'
+  sum: '-1.123e+01'
 network.model.decoder.layers.17.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.251e-01'
-  mean: '1.74e-04'
-  min: '-1.978e-01'
+  mean: '1.749e-04'
+  min: '-1.977e-01'
   shape:
   - 1024
-  sum: '1.781e-01'
+  sum: '1.791e-01'
 network.model.decoder.layers.17.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.18.fc1.bias:
-  device: cuda:0
-  max: '6.793e-02'
+  device: cpu
+  max: '6.791e-02'
   mean: '-1.838e-02'
-  min: '-8.258e-02'
+  min: '-8.256e-02'
   shape:
   - 4096
   sum: '-7.527e+01'
 network.model.decoder.layers.18.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.266e-01'
   mean: '-1.719e-04'
   min: '-1.256e-01'
@@ -1383,40 +1383,40 @@ network.model.decoder.layers.18.fc1.weight:
   - 1024
   sum: '-7.209e+02'
 network.model.decoder.layers.18.fc2.bias:
-  device: cuda:0
-  max: '6.201e-02'
-  mean: '-3.286e-06'
-  min: '-1.06e-01'
+  device: cpu
+  max: '6.203e-02'
+  mean: '-3.168e-06'
+  min: '-1.059e-01'
   shape:
   - 1024
-  sum: '-3.364e-03'
+  sum: '-3.244e-03'
 network.model.decoder.layers.18.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.271e-01'
-  mean: '2.113e-06'
+  mean: '2.159e-06'
   min: '-1.885e-01'
   shape:
   - 1024
   - 4096
-  sum: '8.863e+00'
+  sum: '9.057e+00'
 network.model.decoder.layers.18.final_layer_norm.bias:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '-1.239e-02'
   min: '-1.262e-01'
   shape:
   - 1024
   sum: '-1.268e+01'
 network.model.decoder.layers.18.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
+  mean: '1.000e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.18.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
   mean: '5.307e-03'
   min: '-1.218e-01'
@@ -1424,67 +1424,67 @@ network.model.decoder.layers.18.self_attn.k_proj.bias:
   - 1024
   sum: '5.434e+00'
 network.model.decoder.layers.18.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.26e-01'
-  mean: '1.154e-05'
-  min: '-1.27e-01'
+  mean: '1.155e-05'
+  min: '-1.269e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.210e+01'
+  sum: '1.211e+01'
 network.model.decoder.layers.18.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '7.617e-02'
+  device: cpu
+  max: '7.615e-02'
   mean: '-8.257e-06'
   min: '-1.25e-01'
   shape:
   - 1024
   sum: '-8.455e-03'
 network.model.decoder.layers.18.self_attn.out_proj.weight:
-  device: cuda:0
-  max: '1.453e-01'
-  mean: '-6.184e-06'
+  device: cpu
+  max: '1.452e-01'
+  mean: '-6.174e-06'
   min: '-1.554e-01'
   shape:
   - 1024
   - 1024
-  sum: '-6.484e+00'
+  sum: '-6.474e+00'
 network.model.decoder.layers.18.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.002e-01'
-  mean: '-2.302e-03'
+  mean: '-2.301e-03'
   min: '-1.179e-01'
   shape:
   - 1024
-  sum: '-2.357e+00'
+  sum: '-2.356e+00'
 network.model.decoder.layers.18.self_attn.q_proj.weight:
-  device: cuda:0
-  max: '1.274e-01'
-  mean: '-2.129e-05'
-  min: '-1.27e-01'
+  device: cpu
+  max: '1.275e-01'
+  mean: '-2.130e-05'
+  min: '-1.269e-01'
   shape:
   - 1024
   - 1024
-  sum: '-2.233e+01'
+  sum: '-2.234e+01'
 network.model.decoder.layers.18.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.874e-02'
-  mean: '-1.296e-04'
-  min: '-4.315e-02'
+  device: cpu
+  max: '4.872e-02'
+  mean: '-1.307e-04'
+  min: '-4.313e-02'
   shape:
   - 1024
-  sum: '-1.327e-01'
+  sum: '-1.339e-01'
 network.model.decoder.layers.18.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.249e-01'
-  mean: '-5.472e-05'
-  min: '-1.25e-01'
+  mean: '-5.479e-05'
+  min: '-1.250e-01'
   shape:
   - 1024
   - 1024
-  sum: '-5.738e+01'
+  sum: '-5.745e+01'
 network.model.decoder.layers.18.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.251e-01'
   mean: '1.729e-03'
   min: '-1.528e-01'
@@ -1492,158 +1492,158 @@ network.model.decoder.layers.18.self_attn_layer_norm.bias:
   - 1024
   sum: '1.771e+00'
 network.model.decoder.layers.18.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.19.fc1.bias:
-  device: cuda:0
+  device: cpu
   max: '9.674e-02'
   mean: '-1.617e-02'
-  min: '-7.123e-02'
+  min: '-7.121e-02'
   shape:
   - 4096
-  sum: '-6.623e+01'
+  sum: '-6.624e+01'
 network.model.decoder.layers.19.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.276e-01'
   mean: '-1.816e-04'
   min: '-1.266e-01'
   shape:
   - 4096
   - 1024
-  sum: '-7.616e+02'
+  sum: '-7.617e+02'
 network.model.decoder.layers.19.fc2.bias:
-  device: cuda:0
-  max: '6.439e-02'
-  mean: '-2.292e-04'
-  min: '-7.587e-02'
+  device: cpu
+  max: '6.441e-02'
+  mean: '-2.289e-04'
+  min: '-7.589e-02'
   shape:
   - 1024
-  sum: '-2.347e-01'
+  sum: '-2.344e-01'
 network.model.decoder.layers.19.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.273e-01'
-  mean: '6.639e-06'
+  mean: '6.625e-06'
   min: '-1.782e-01'
   shape:
   - 1024
   - 4096
-  sum: '2.785e+01'
+  sum: '2.779e+01'
 network.model.decoder.layers.19.final_layer_norm.bias:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '-9.252e-03'
-  min: '-1.25e-01'
+  min: '-1.250e-01'
   shape:
   - 1024
   sum: '-9.474e+00'
 network.model.decoder.layers.19.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.19.self_attn.k_proj.bias:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '7.829e-03'
-  min: '-1.25e-01'
+  min: '-1.250e-01'
   shape:
   - 1024
   sum: '8.017e+00'
 network.model.decoder.layers.19.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.265e-01'
-  mean: '-2.187e-05'
+  mean: '-2.188e-05'
   min: '-1.265e-01'
   shape:
   - 1024
   - 1024
   sum: '-2.294e+01'
 network.model.decoder.layers.19.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '6.445e-02'
-  mean: '2.324e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '6.447e-02'
+  mean: '2.320e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '2.380e-01'
+  sum: '2.376e-01'
 network.model.decoder.layers.19.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.454e-01'
-  mean: '-5.801e-08'
-  min: '-1.431e-01'
+  mean: '-4.602e-08'
+  min: '-1.430e-01'
   shape:
   - 1024
   - 1024
-  sum: '-6.083e-02'
+  sum: '-4.826e-02'
 network.model.decoder.layers.19.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.252e-01'
-  mean: '-2.284e-03'
-  min: '-1.25e-01'
+  mean: '-2.283e-03'
+  min: '-1.250e-01'
   shape:
   - 1024
   sum: '-2.338e+00'
 network.model.decoder.layers.19.self_attn.q_proj.weight:
-  device: cuda:0
-  max: '1.276e-01'
-  mean: '8.971e-05'
-  min: '-1.281e-01'
+  device: cpu
+  max: '1.275e-01'
+  mean: '8.968e-05'
+  min: '-1.280e-01'
   shape:
   - 1024
   - 1024
-  sum: '9.406e+01'
+  sum: '9.404e+01'
 network.model.decoder.layers.19.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.413e-02'
-  mean: '-1.693e-04'
-  min: '-4.315e-02'
+  device: cpu
+  max: '4.411e-02'
+  mean: '-1.694e-04'
+  min: '-4.313e-02'
   shape:
   - 1024
-  sum: '-1.733e-01'
+  sum: '-1.735e-01'
 network.model.decoder.layers.19.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.249e-01'
-  mean: '-6.37e-05'
+  mean: '-6.369e-05'
   min: '-1.249e-01'
   shape:
   - 1024
   - 1024
-  sum: '-6.679e+01'
+  sum: '-6.678e+01'
 network.model.decoder.layers.19.self_attn_layer_norm.bias:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '3.325e-03'
   min: '-1.936e-01'
   shape:
   - 1024
-  sum: '3.405e+00'
+  sum: '3.404e+00'
 network.model.decoder.layers.19.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.2.fc1.bias:
-  device: cuda:0
-  max: '7.135e-02'
-  mean: '-2.341e-02'
-  min: '-6.665e-02'
+  device: cpu
+  max: '7.137e-02'
+  mean: '-2.342e-02'
+  min: '-6.663e-02'
   shape:
   - 4096
   sum: '-9.591e+01'
 network.model.decoder.layers.2.fc1.weight:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '2.334e-04'
   min: '-1.255e-01'
   shape:
@@ -1651,40 +1651,40 @@ network.model.decoder.layers.2.fc1.weight:
   - 1024
   sum: '9.791e+02'
 network.model.decoder.layers.2.fc2.bias:
-  device: cuda:0
-  max: '7.172e-02'
-  mean: '3.129e-04'
-  min: '-7.66e-02'
+  device: cpu
+  max: '7.17e-02'
+  mean: '3.127e-04'
+  min: '-7.658e-02'
   shape:
   - 1024
-  sum: '3.204e-01'
+  sum: '3.202e-01'
 network.model.decoder.layers.2.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.294e-01'
-  mean: '-1.695e-06'
+  mean: '-1.673e-06'
   min: '-2.5e-01'
   shape:
   - 1024
   - 4096
-  sum: '-7.109e+00'
+  sum: '-7.019e+00'
 network.model.decoder.layers.2.final_layer_norm.bias:
-  device: cuda:0
-  max: '1.257e-01'
+  device: cpu
+  max: '1.258e-01'
   mean: '9.144e-03'
   min: '-1.251e-01'
   shape:
   - 1024
-  sum: '9.364e+00'
+  sum: '9.363e+00'
 network.model.decoder.layers.2.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
+  mean: '1.000e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.2.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '6.384e-02'
   mean: '8.869e-03'
   min: '-6.445e-02'
@@ -1692,42 +1692,42 @@ network.model.decoder.layers.2.self_attn.k_proj.bias:
   - 1024
   sum: '9.082e+00'
 network.model.decoder.layers.2.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.292e-01'
   mean: '2.489e-05'
   min: '-1.265e-01'
   shape:
   - 1024
   - 1024
-  sum: '2.61e+01'
+  sum: '2.610e+01'
 network.model.decoder.layers.2.self_attn.out_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.234e-01'
-  mean: '3.411e-04'
-  min: '-8.948e-02'
+  mean: '3.406e-04'
+  min: '-8.946e-02'
   shape:
   - 1024
-  sum: '3.493e-01'
+  sum: '3.488e-01'
 network.model.decoder.layers.2.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.317e-01'
-  mean: '-6.495e-06'
+  mean: '-6.526e-06'
   min: '-1.283e-01'
   shape:
   - 1024
   - 1024
-  sum: '-6.811e+00'
+  sum: '-6.842e+00'
 network.model.decoder.layers.2.self_attn.q_proj.bias:
-  device: cuda:0
-  max: '1.249e-01'
-  mean: '9.792e-04'
+  device: cpu
+  max: '1.25e-01'
+  mean: '9.793e-04'
   min: '-1.255e-01'
   shape:
   - 1024
   sum: '1.003e+00'
 network.model.decoder.layers.2.self_attn.q_proj.weight:
-  device: cuda:0
-  max: '1.257e-01'
+  device: cpu
+  max: '1.258e-01'
   mean: '1.202e-05'
   min: '-1.271e-01'
   shape:
@@ -1735,316 +1735,316 @@ network.model.decoder.layers.2.self_attn.q_proj.weight:
   - 1024
   sum: '1.260e+01'
 network.model.decoder.layers.2.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.211e-02'
-  mean: '-9.478e-05'
-  min: '-3.799e-02'
+  device: cpu
+  max: '4.209e-02'
+  mean: '-9.553e-05'
+  min: '-3.797e-02'
   shape:
   - 1024
-  sum: '-9.706e-02'
+  sum: '-9.782e-02'
 network.model.decoder.layers.2.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.234e-01'
-  mean: '3.971e-05'
-  min: '-1.171e-01'
+  mean: '3.973e-05'
+  min: '-1.170e-01'
   shape:
   - 1024
   - 1024
-  sum: '4.164e+01'
+  sum: '4.166e+01'
 network.model.decoder.layers.2.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.309e-01'
   mean: '-1.911e-03'
-  min: '-1.254e-01'
+  min: '-1.253e-01'
   shape:
   - 1024
   sum: '-1.957e+00'
 network.model.decoder.layers.2.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
+  mean: '1.000e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.20.fc1.bias:
-  device: cuda:0
-  max: '7.928e-02'
-  mean: '-1.524e-02'
-  min: '-7.220e-02'
+  device: cpu
+  max: '7.926e-02'
+  mean: '-1.525e-02'
+  min: '-7.222e-02'
   shape:
   - 4096
   sum: '-6.244e+01'
 network.model.decoder.layers.20.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.277e-01'
   mean: '-1.853e-04'
   min: '-1.271e-01'
   shape:
   - 4096
   - 1024
-  sum: '-7.770e+02'
+  sum: '-7.771e+02'
 network.model.decoder.layers.20.fc2.bias:
-  device: cuda:0
-  max: '6.787e-02'
-  mean: '-1.132e-04'
-  min: '-7.617e-02'
+  device: cpu
+  max: '6.789e-02'
+  mean: '-1.129e-04'
+  min: '-7.619e-02'
   shape:
   - 1024
-  sum: '-1.159e-01'
+  sum: '-1.156e-01'
 network.model.decoder.layers.20.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.27e-01'
-  mean: '6.366e-06'
+  mean: '6.370e-06'
   min: '-2.393e-01'
   shape:
   - 1024
   - 4096
-  sum: '2.670e+01'
+  sum: '2.672e+01'
 network.model.decoder.layers.20.final_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
   mean: '-9.149e-03'
-  min: '-1.25e-01'
+  min: '-1.250e-01'
   shape:
   - 1024
   sum: '-9.369e+00'
 network.model.decoder.layers.20.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.20.self_attn.k_proj.bias:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '1.126e-02'
   min: '-1.25e-01'
   shape:
   - 1024
   sum: '1.153e+01'
 network.model.decoder.layers.20.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.356e-01'
-  mean: '4.825e-05'
+  mean: '4.827e-05'
   min: '-1.333e-01'
   shape:
   - 1024
   - 1024
-  sum: '5.059e+01'
+  sum: '5.061e+01'
 network.model.decoder.layers.20.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '6.512e-02'
-  mean: '-8.754e-05'
+  device: cpu
+  max: '6.510e-02'
+  mean: '-8.726e-05'
   min: '-1.215e-01'
   shape:
   - 1024
-  sum: '-8.964e-02'
+  sum: '-8.936e-02'
 network.model.decoder.layers.20.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.334e-01'
-  mean: '8.321e-06'
+  mean: '8.325e-06'
   min: '-1.311e-01'
   shape:
   - 1024
   - 1024
-  sum: '8.725e+00'
+  sum: '8.729e+00'
 network.model.decoder.layers.20.self_attn.q_proj.bias:
-  device: cuda:0
-  max: '1.252e-01'
-  mean: '-2.386e-03'
+  device: cpu
+  max: '1.253e-01'
+  mean: '-2.388e-03'
   min: '-1.256e-01'
   shape:
   - 1024
-  sum: '-2.444e+00'
+  sum: '-2.445e+00'
 network.model.decoder.layers.20.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.278e-01'
-  mean: '1.178e-07'
+  mean: '9.913e-08'
   min: '-1.279e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.235e-01'
+  sum: '1.039e-01'
 network.model.decoder.layers.20.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.395e-02'
-  mean: '-3.544e-04'
-  min: '-4.248e-02'
+  device: cpu
+  max: '4.397e-02'
+  mean: '-3.546e-04'
+  min: '-4.246e-02'
   shape:
   - 1024
-  sum: '-3.629e-01'
+  sum: '-3.631e-01'
 network.model.decoder.layers.20.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.246e-01'
-  mean: '1.676e-06'
+  mean: '1.575e-06'
   min: '-1.249e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.757e+00'
+  sum: '1.651e+00'
 network.model.decoder.layers.20.self_attn_layer_norm.bias:
-  device: cuda:0
-  max: '1.25e-01'
-  mean: '3.003e-03'
+  device: cpu
+  max: '1.250e-01'
+  mean: '3.004e-03'
   min: '-1.256e-01'
   shape:
   - 1024
-  sum: '3.075e+00'
+  sum: '3.076e+00'
 network.model.decoder.layers.20.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
+  mean: '1.000e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.21.fc1.bias:
-  device: cuda:0
+  device: cpu
   max: '8.362e-02'
   mean: '-1.634e-02'
-  min: '-9.613e-02'
+  min: '-9.615e-02'
   shape:
   - 4096
   sum: '-6.693e+01'
 network.model.decoder.layers.21.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.289e-01'
   mean: '-1.814e-04'
   min: '-1.299e-01'
   shape:
   - 4096
   - 1024
-  sum: '-7.611e+02'
+  sum: '-7.610e+02'
 network.model.decoder.layers.21.fc2.bias:
-  device: cuda:0
-  max: '9.045e-02'
-  mean: '5.474e-05'
-  min: '-7.306e-02'
+  device: cpu
+  max: '9.043e-02'
+  mean: '5.509e-05'
+  min: '-7.308e-02'
   shape:
   - 1024
-  sum: '5.605e-02'
+  sum: '5.641e-02'
 network.model.decoder.layers.21.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.322e-01'
-  mean: '3.575e-07'
-  min: '-2.5e-01'
+  mean: '3.543e-07'
+  min: '-2.500e-01'
   shape:
   - 1024
   - 4096
-  sum: '1.499e+00'
+  sum: '1.486e+00'
 network.model.decoder.layers.21.final_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
   mean: '-5.773e-03'
-  min: '-1.249e-01'
+  min: '-1.25e-01'
   shape:
   - 1024
-  sum: '-5.912e+00'
+  sum: '-5.911e+00'
 network.model.decoder.layers.21.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
+  mean: '1.000e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.21.self_attn.k_proj.bias:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '9.81e-03'
   min: '-1.318e-01'
   shape:
   - 1024
   sum: '1.005e+01'
 network.model.decoder.layers.21.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.425e-01'
-  mean: '-2.337e-05'
+  mean: '-2.334e-05'
   min: '-1.454e-01'
   shape:
   - 1024
   - 1024
-  sum: '-2.450e+01'
+  sum: '-2.447e+01'
 network.model.decoder.layers.21.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '7.263e-02'
-  mean: '-6.624e-05'
-  min: '-9.937e-02'
+  device: cpu
+  max: '7.261e-02'
+  mean: '-6.581e-05'
+  min: '-9.939e-02'
   shape:
   - 1024
-  sum: '-6.783e-02'
+  sum: '-6.739e-02'
 network.model.decoder.layers.21.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.294e-01'
-  mean: '1.762e-06'
-  min: '-1.285e-01'
+  mean: '1.757e-06'
+  min: '-1.286e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.847e+00'
+  sum: '1.842e+00'
 network.model.decoder.layers.21.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.257e-01'
-  mean: '-1.89e-03'
+  mean: '-1.890e-03'
   min: '-1.257e-01'
   shape:
   - 1024
-  sum: '-1.935e+00'
+  sum: '-1.936e+00'
 network.model.decoder.layers.21.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.327e-01'
-  mean: '-1.882e-05'
-  min: '-1.31e-01'
+  mean: '-1.881e-05'
+  min: '-1.310e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.974e+01'
+  sum: '-1.973e+01'
 network.model.decoder.layers.21.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.669e-02'
-  mean: '-2.74e-04'
-  min: '-4.211e-02'
+  device: cpu
+  max: '4.667e-02'
+  mean: '-2.739e-04'
+  min: '-4.213e-02'
   shape:
   - 1024
-  sum: '-2.806e-01'
+  sum: '-2.804e-01'
 network.model.decoder.layers.21.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
-  mean: '-7.892e-05'
+  mean: '-7.890e-05'
   min: '-1.249e-01'
   shape:
   - 1024
   - 1024
-  sum: '-8.276e+01'
+  sum: '-8.273e+01'
 network.model.decoder.layers.21.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
-  mean: '3.155e-03'
-  min: '-1.25e-01'
+  mean: '3.156e-03'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '3.231e+00'
+  sum: '3.232e+00'
 network.model.decoder.layers.21.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.22.fc1.bias:
-  device: cuda:0
+  device: cpu
   max: '1.251e-01'
   mean: '-1.548e-02'
   min: '-1.254e-01'
   shape:
   - 4096
-  sum: '-6.341e+01'
+  sum: '-6.342e+01'
 network.model.decoder.layers.22.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.278e-01'
   mean: '-1.567e-04'
   min: '-1.277e-01'
@@ -2053,74 +2053,74 @@ network.model.decoder.layers.22.fc1.weight:
   - 1024
   sum: '-6.574e+02'
 network.model.decoder.layers.22.fc2.bias:
-  device: cuda:0
-  max: '7.642e-02'
-  mean: '1.103e-04'
-  min: '-7.037e-02'
+  device: cpu
+  max: '7.64e-02'
+  mean: '1.105e-04'
+  min: '-7.035e-02'
   shape:
   - 1024
-  sum: '1.13e-01'
+  sum: '1.132e-01'
 network.model.decoder.layers.22.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.279e-01'
-  mean: '1.737e-06'
+  mean: '1.739e-06'
   min: '-1.288e-01'
   shape:
   - 1024
   - 4096
-  sum: '7.287e+00'
+  sum: '7.293e+00'
 network.model.decoder.layers.22.final_layer_norm.bias:
-  device: cuda:0
-  max: '1.25e-01'
-  mean: '-4.785e-03'
-  min: '-1.25e-01'
+  device: cpu
+  max: '1.250e-01'
+  mean: '-4.784e-03'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '-4.9e+00'
+  sum: '-4.899e+00'
 network.model.decoder.layers.22.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
+  mean: '1.000e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.22.self_attn.k_proj.bias:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '6.801e-03'
   min: '-1.25e-01'
   shape:
   - 1024
   sum: '6.964e+00'
 network.model.decoder.layers.22.self_attn.k_proj.weight:
-  device: cuda:0
-  max: '1.401e-01'
-  mean: '-8.573e-06'
+  device: cpu
+  max: '1.402e-01'
+  mean: '-8.575e-06'
   min: '-1.409e-01'
   shape:
   - 1024
   - 1024
-  sum: '-8.99e+00'
+  sum: '-8.991e+00'
 network.model.decoder.layers.22.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '7.709e-02'
-  mean: '-1.158e-05'
-  min: '-8.099e-02'
+  device: cpu
+  max: '7.707e-02'
+  mean: '-1.177e-05'
+  min: '-8.101e-02'
   shape:
   - 1024
-  sum: '-1.186e-02'
+  sum: '-1.206e-02'
 network.model.decoder.layers.22.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.302e-01'
-  mean: '-1.088e-06'
+  mean: '-1.093e-06'
   min: '-1.293e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.141e+00'
+  sum: '-1.146e+00'
 network.model.decoder.layers.22.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.013e-01'
   mean: '-1.666e-03'
   min: '-1.021e-01'
@@ -2128,99 +2128,99 @@ network.model.decoder.layers.22.self_attn.q_proj.bias:
   - 1024
   sum: '-1.706e+00'
 network.model.decoder.layers.22.self_attn.q_proj.weight:
-  device: cuda:0
-  max: '1.331e-01'
+  device: cpu
+  max: '1.330e-01'
   mean: '-2.958e-05'
   min: '-1.338e-01'
   shape:
   - 1024
   - 1024
-  sum: '-3.102e+01'
+  sum: '-3.101e+01'
 network.model.decoder.layers.22.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.211e-02'
-  mean: '5.506e-04'
-  min: '-4.501e-02'
+  device: cpu
+  max: '4.209e-02'
+  mean: '5.509e-04'
+  min: '-4.499e-02'
   shape:
   - 1024
-  sum: '5.638e-01'
+  sum: '5.641e-01'
 network.model.decoder.layers.22.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.257e-01'
-  mean: '-2.981e-05'
+  mean: '-2.983e-05'
   min: '-1.25e-01'
   shape:
   - 1024
   - 1024
-  sum: '-3.125e+01'
+  sum: '-3.128e+01'
 network.model.decoder.layers.22.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
-  mean: '7.961e-04'
-  min: '-1.25e-01'
+  mean: '7.960e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '8.152e-01'
+  sum: '8.151e-01'
 network.model.decoder.layers.22.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.23.fc1.bias:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '2.694e-03'
   min: '-1.278e-01'
   shape:
   - 4096
-  sum: '1.103e+01'
+  sum: '1.104e+01'
 network.model.decoder.layers.23.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '2.107e-01'
-  mean: '8.400e-05'
+  mean: '8.401e-05'
   min: '-2.146e-01'
   shape:
   - 4096
   - 1024
-  sum: '3.523e+02'
+  sum: '3.524e+02'
 network.model.decoder.layers.23.fc2.bias:
-  device: cuda:0
-  max: '6.299e-02'
+  device: cpu
+  max: '6.297e-02'
   mean: '1.316e-03'
-  min: '-6.311e-02'
+  min: '-6.313e-02'
   shape:
   - 1024
-  sum: '1.348e+00'
+  sum: '1.347e+00'
 network.model.decoder.layers.23.fc2.weight:
-  device: cuda:0
-  max: '2.5e-01'
-  mean: '1.024e-05'
-  min: '-2.5e-01'
+  device: cpu
+  max: '2.500e-01'
+  mean: '1.027e-05'
+  min: '-2.500e-01'
   shape:
   - 1024
   - 4096
-  sum: '4.294e+01'
+  sum: '4.31e+01'
 network.model.decoder.layers.23.final_layer_norm.bias:
-  device: cuda:0
-  max: '7.251e-02'
-  mean: '9.345e-03'
-  min: '-7.196e-02'
+  device: cpu
+  max: '7.253e-02'
+  mean: '9.346e-03'
+  min: '-7.194e-02'
   shape:
   - 1024
-  sum: '9.57e+00'
+  sum: '9.570e+00'
 network.model.decoder.layers.23.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.23.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '2.219e-01'
   mean: '3.647e-03'
   min: '-1.824e-01'
@@ -2228,7 +2228,7 @@ network.model.decoder.layers.23.self_attn.k_proj.bias:
   - 1024
   sum: '3.734e+00'
 network.model.decoder.layers.23.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.294e-01'
   mean: '-1.63e-05'
   min: '-1.304e-01'
@@ -2237,32 +2237,32 @@ network.model.decoder.layers.23.self_attn.k_proj.weight:
   - 1024
   sum: '-1.709e+01'
 network.model.decoder.layers.23.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '7.605e-02'
-  mean: '-1.183e-04'
-  min: '-6.47e-02'
+  device: cpu
+  max: '7.607e-02'
+  mean: '-1.182e-04'
+  min: '-6.468e-02'
   shape:
   - 1024
-  sum: '-1.212e-01'
+  sum: '-1.210e-01'
 network.model.decoder.layers.23.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '2.5e-01'
-  mean: '-1.078e-05'
+  mean: '-1.079e-05'
   min: '-2.5e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.130e+01'
+  sum: '-1.131e+01'
 network.model.decoder.layers.23.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
-  mean: '-2.744e-04'
+  mean: '-2.745e-04'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '-2.809e-01'
+  sum: '-2.811e-01'
 network.model.decoder.layers.23.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.338e-01'
   mean: '2.096e-05'
   min: '-1.337e-01'
@@ -2271,90 +2271,90 @@ network.model.decoder.layers.23.self_attn.q_proj.weight:
   - 1024
   sum: '2.197e+01'
 network.model.decoder.layers.23.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.068e-02'
-  mean: '2.158e-05'
-  min: '-4.48e-02'
+  device: cpu
+  max: '4.066e-02'
+  mean: '2.115e-05'
+  min: '-4.482e-02'
   shape:
   - 1024
-  sum: '2.210e-02'
+  sum: '2.166e-02'
 network.model.decoder.layers.23.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.267e-01'
-  mean: '6.273e-05'
+  mean: '6.276e-05'
   min: '-1.256e-01'
   shape:
   - 1024
   - 1024
-  sum: '6.577e+01'
+  sum: '6.581e+01'
 network.model.decoder.layers.23.self_attn_layer_norm.bias:
-  device: cuda:0
-  max: '1.25e-01'
-  mean: '1.700e-03'
+  device: cpu
+  max: '1.250e-01'
+  mean: '1.7e-03'
   min: '-1.25e-01'
   shape:
   - 1024
   sum: '1.741e+00'
 network.model.decoder.layers.23.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.3.fc1.bias:
-  device: cuda:0
-  max: '8.453e-02'
+  device: cpu
+  max: '8.451e-02'
   mean: '-2.474e-02'
   min: '-1.194e-01'
   shape:
   - 4096
   sum: '-1.013e+02'
 network.model.decoder.layers.3.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.251e-01'
   mean: '1.348e-04'
-  min: '-1.252e-01'
+  min: '-1.253e-01'
   shape:
   - 4096
   - 1024
-  sum: '5.654e+02'
+  sum: '5.655e+02'
 network.model.decoder.layers.3.fc2.bias:
-  device: cuda:0
-  max: '7.086e-02'
-  mean: '1.769e-04'
+  device: cpu
+  max: '7.084e-02'
+  mean: '1.768e-04'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '1.811e-01'
+  sum: '1.810e-01'
 network.model.decoder.layers.3.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.276e-01'
-  mean: '1.857e-06'
+  mean: '1.840e-06'
   min: '-2.5e-01'
   shape:
   - 1024
   - 4096
-  sum: '7.790e+00'
+  sum: '7.72e+00'
 network.model.decoder.layers.3.final_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.254e-01'
-  mean: '6.555e-03'
-  min: '-1.254e-01'
+  mean: '6.554e-03'
+  min: '-1.253e-01'
   shape:
   - 1024
-  sum: '6.712e+00'
+  sum: '6.711e+00'
 network.model.decoder.layers.3.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
+  mean: '1.000e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.3.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '6.372e-02'
   mean: '8.278e-03'
   min: '-3.555e-02'
@@ -2362,92 +2362,92 @@ network.model.decoder.layers.3.self_attn.k_proj.bias:
   - 1024
   sum: '8.477e+00'
 network.model.decoder.layers.3.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.266e-01'
-  mean: '-1.901e-05'
+  mean: '-1.902e-05'
   min: '-1.266e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.993e+01'
+  sum: '-1.994e+01'
 network.model.decoder.layers.3.self_attn.out_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.240e-01'
-  mean: '1.084e-04'
+  mean: '1.082e-04'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '1.11e-01'
+  sum: '1.108e-01'
 network.model.decoder.layers.3.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.764e-01'
-  mean: '-1.601e-06'
+  mean: '-1.6e-06'
   min: '-1.614e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.679e+00'
+  sum: '-1.677e+00'
 network.model.decoder.layers.3.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.248e-01'
-  mean: '-2.804e-04'
+  mean: '-2.811e-04'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '-2.871e-01'
+  sum: '-2.879e-01'
 network.model.decoder.layers.3.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.266e-01'
-  mean: '-1.642e-05'
+  mean: '-1.641e-05'
   min: '-1.266e-01'
   shape:
   - 1024
   - 1024
   sum: '-1.721e+01'
 network.model.decoder.layers.3.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '3.882e-02'
-  mean: '-9.93e-04'
-  min: '-4.312e-02'
+  device: cpu
+  max: '3.884e-02'
+  mean: '-9.932e-04'
+  min: '-4.310e-02'
   shape:
   - 1024
   sum: '-1.017e+00'
 network.model.decoder.layers.3.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.216e-01'
-  mean: '-9.011e-05'
+  mean: '-9.016e-05'
   min: '-1.204e-01'
   shape:
   - 1024
   - 1024
-  sum: '-9.449e+01'
+  sum: '-9.454e+01'
 network.model.decoder.layers.3.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.290e-01'
-  mean: '-4.648e-04'
-  min: '-1.259e-01'
+  mean: '-4.653e-04'
+  min: '-1.258e-01'
   shape:
   - 1024
-  sum: '-4.76e-01'
+  sum: '-4.764e-01'
 network.model.decoder.layers.3.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
+  mean: '1.000e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.4.fc1.bias:
-  device: cuda:0
-  max: '7.648e-02'
+  device: cpu
+  max: '7.65e-02'
   mean: '-2.333e-02'
   min: '-1.11e-01'
   shape:
   - 4096
-  sum: '-9.556e+01'
+  sum: '-9.557e+01'
 network.model.decoder.layers.4.fc1.weight:
-  device: cuda:0
-  max: '1.252e-01'
+  device: cpu
+  max: '1.253e-01'
   mean: '7.858e-05'
   min: '-1.261e-01'
   shape:
@@ -2455,40 +2455,40 @@ network.model.decoder.layers.4.fc1.weight:
   - 1024
   sum: '3.296e+02'
 network.model.decoder.layers.4.fc2.bias:
-  device: cuda:0
-  max: '6.671e-02'
-  mean: '6.644e-04'
+  device: cpu
+  max: '6.669e-02'
+  mean: '6.65e-04'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '6.803e-01'
+  sum: '6.809e-01'
 network.model.decoder.layers.4.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.281e-01'
-  mean: '2.081e-06'
+  mean: '2.073e-06'
   min: '-2.5e-01'
   shape:
   - 1024
   - 4096
-  sum: '8.729e+00'
+  sum: '8.694e+00'
 network.model.decoder.layers.4.final_layer_norm.bias:
-  device: cuda:0
-  max: '1.25e-01'
-  mean: '2.551e-03'
-  min: '-1.259e-01'
+  device: cpu
+  max: '1.250e-01'
+  mean: '2.552e-03'
+  min: '-1.258e-01'
   shape:
   - 1024
   sum: '2.613e+00'
 network.model.decoder.layers.4.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.4.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '6.433e-02'
   mean: '9.123e-03'
   min: '-6.219e-02'
@@ -2496,133 +2496,133 @@ network.model.decoder.layers.4.self_attn.k_proj.bias:
   - 1024
   sum: '9.342e+00'
 network.model.decoder.layers.4.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.298e-01'
-  mean: '3.159e-05'
+  mean: '3.157e-05'
   min: '-1.27e-01'
   shape:
   - 1024
   - 1024
-  sum: '3.312e+01'
+  sum: '3.310e+01'
 network.model.decoder.layers.4.self_attn.out_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.113e-01'
-  mean: '3.284e-04'
+  mean: '3.290e-04'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '3.363e-01'
+  sum: '3.369e-01'
 network.model.decoder.layers.4.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.307e-01'
-  mean: '5.154e-06'
+  mean: '5.178e-06'
   min: '-1.296e-01'
   shape:
   - 1024
   - 1024
-  sum: '5.404e+00'
+  sum: '5.429e+00'
 network.model.decoder.layers.4.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.251e-01'
   mean: '1.442e-03'
-  min: '-1.25e-01'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '1.477e+00'
+  sum: '1.476e+00'
 network.model.decoder.layers.4.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.277e-01'
-  mean: '-1.649e-06'
+  mean: '-1.645e-06'
   min: '-1.267e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.729e+00'
+  sum: '-1.725e+00'
 network.model.decoder.layers.4.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '3.711e-02'
-  mean: '1.497e-04'
-  min: '-3.909e-02'
+  device: cpu
+  max: '3.709e-02'
+  mean: '1.498e-04'
+  min: '-3.907e-02'
   shape:
   - 1024
-  sum: '1.533e-01'
+  sum: '1.534e-01'
 network.model.decoder.layers.4.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.139e-01'
-  mean: '6.411e-05'
+  mean: '6.417e-05'
   min: '-1.227e-01'
   shape:
   - 1024
   - 1024
-  sum: '6.722e+01'
+  sum: '6.729e+01'
 network.model.decoder.layers.4.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.271e-01'
-  mean: '1.923e-04'
+  mean: '1.930e-04'
   min: '-1.272e-01'
   shape:
   - 1024
-  sum: '1.969e-01'
+  sum: '1.976e-01'
 network.model.decoder.layers.4.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
+  mean: '1.000e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.5.fc1.bias:
-  device: cuda:0
-  max: '9.772e-02'
-  mean: '-2.182e-02'
-  min: '-1.219e-01'
+  device: cpu
+  max: '9.77e-02'
+  mean: '-2.183e-02'
+  min: '-1.22e-01'
   shape:
   - 4096
   sum: '-8.94e+01'
 network.model.decoder.layers.5.fc1.weight:
-  device: cuda:0
-  max: '1.257e-01'
+  device: cpu
+  max: '1.258e-01'
   mean: '1.105e-04'
   min: '-1.254e-01'
   shape:
   - 4096
   - 1024
-  sum: '4.637e+02'
+  sum: '4.636e+02'
 network.model.decoder.layers.5.fc2.bias:
-  device: cuda:0
-  max: '6.384e-02'
-  mean: '9.162e-05'
+  device: cpu
+  max: '6.382e-02'
+  mean: '9.193e-05'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '9.382e-02'
+  sum: '9.414e-02'
 network.model.decoder.layers.5.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.262e-01'
-  mean: '4.982e-07'
-  min: '-2.5e-01'
+  mean: '5.023e-07'
+  min: '-2.500e-01'
   shape:
   - 1024
   - 4096
-  sum: '2.089e+00'
+  sum: '2.107e+00'
 network.model.decoder.layers.5.final_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
-  mean: '4.158e-04'
+  mean: '4.163e-04'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '4.258e-01'
+  sum: '4.263e-01'
 network.model.decoder.layers.5.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.5.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '7.245e-02'
   mean: '1.13e-02'
   min: '-5.319e-02'
@@ -2630,133 +2630,133 @@ network.model.decoder.layers.5.self_attn.k_proj.bias:
   - 1024
   sum: '1.157e+01'
 network.model.decoder.layers.5.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.263e-01'
-  mean: '-5.184e-05'
+  mean: '-5.180e-05'
   min: '-1.263e-01'
   shape:
   - 1024
   - 1024
-  sum: '-5.436e+01'
+  sum: '-5.432e+01'
 network.model.decoder.layers.5.self_attn.out_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.068e-01'
-  mean: '2.054e-04'
+  mean: '2.058e-04'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '2.103e-01'
+  sum: '2.108e-01'
 network.model.decoder.layers.5.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.582e-01'
-  mean: '2.069e-05'
+  mean: '2.068e-05'
   min: '-1.821e-01'
   shape:
   - 1024
   - 1024
-  sum: '2.169e+01'
+  sum: '2.168e+01'
 network.model.decoder.layers.5.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
-  mean: '-6.643e-04'
-  min: '-1.254e-01'
+  mean: '-6.650e-04'
+  min: '-1.253e-01'
   shape:
   - 1024
-  sum: '-6.802e-01'
+  sum: '-6.81e-01'
 network.model.decoder.layers.5.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.261e-01'
-  mean: '1.035e-05'
+  mean: '1.04e-05'
   min: '-1.27e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.086e+01'
+  sum: '1.090e+01'
 network.model.decoder.layers.5.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.800e-02'
+  device: cpu
+  max: '4.802e-02'
   mean: '5.821e-04'
-  min: '-4.202e-02'
+  min: '-4.200e-02'
   shape:
   - 1024
-  sum: '5.960e-01'
+  sum: '5.961e-01'
 network.model.decoder.layers.5.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.182e-01'
-  mean: '1.019e-05'
+  mean: '1.011e-05'
   min: '-1.202e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.068e+01'
+  sum: '1.061e+01'
 network.model.decoder.layers.5.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.263e-01'
-  mean: '-4.794e-04'
+  mean: '-4.785e-04'
   min: '-1.257e-01'
   shape:
   - 1024
-  sum: '-4.909e-01'
+  sum: '-4.900e-01'
 network.model.decoder.layers.5.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.6.fc1.bias:
-  device: cuda:0
+  device: cpu
   max: '1.191e-01'
   mean: '-2.029e-02'
-  min: '-9.454e-02'
+  min: '-9.456e-02'
   shape:
   - 4096
   sum: '-8.312e+01'
 network.model.decoder.layers.6.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.282e-01'
   mean: '1.416e-04'
   min: '-1.27e-01'
   shape:
   - 4096
   - 1024
-  sum: '5.939e+02'
+  sum: '5.938e+02'
 network.model.decoder.layers.6.fc2.bias:
-  device: cuda:0
-  max: '6.439e-02'
-  mean: '-1.532e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '6.441e-02'
+  mean: '-1.534e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '-1.569e-01'
+  sum: '-1.571e-01'
 network.model.decoder.layers.6.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.343e-01'
-  mean: '-3.220e-07'
-  min: '-2.5e-01'
+  mean: '-3.184e-07'
+  min: '-2.500e-01'
   shape:
   - 1024
   - 4096
-  sum: '-1.351e+00'
+  sum: '-1.335e+00'
 network.model.decoder.layers.6.final_layer_norm.bias:
-  device: cuda:0
-  max: '1.25e-01'
-  mean: '-1.357e-04'
-  min: '-1.254e-01'
+  device: cpu
+  max: '1.250e-01'
+  mean: '-1.360e-04'
+  min: '-1.253e-01'
   shape:
   - 1024
-  sum: '-1.389e-01'
+  sum: '-1.393e-01'
 network.model.decoder.layers.6.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.6.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '8.856e-02'
   mean: '1.296e-02'
   min: '-6.641e-02'
@@ -2764,33 +2764,33 @@ network.model.decoder.layers.6.self_attn.k_proj.bias:
   - 1024
   sum: '1.327e+01'
 network.model.decoder.layers.6.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.300e-01'
-  mean: '1.62e-05'
+  mean: '1.622e-05'
   min: '-1.300e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.698e+01'
+  sum: '1.701e+01'
 network.model.decoder.layers.6.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '6.47e-02'
-  mean: '-1.618e-04'
+  device: cpu
+  max: '6.468e-02'
+  mean: '-1.613e-04'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '-1.657e-01'
+  sum: '-1.652e-01'
 network.model.decoder.layers.6.self_attn.out_proj.weight:
-  device: cuda:0
-  max: '1.340e-01'
-  mean: '9.419e-06'
+  device: cpu
+  max: '1.341e-01'
+  mean: '9.403e-06'
   min: '-1.305e-01'
   shape:
   - 1024
   - 1024
-  sum: '9.877e+00'
+  sum: '9.859e+00'
 network.model.decoder.layers.6.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.256e-01'
   mean: '2.037e-03'
   min: '-1.257e-01'
@@ -2798,99 +2798,99 @@ network.model.decoder.layers.6.self_attn.q_proj.bias:
   - 1024
   sum: '2.086e+00'
 network.model.decoder.layers.6.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.272e-01'
-  mean: '4.741e-06'
+  mean: '4.712e-06'
   min: '-1.276e-01'
   shape:
   - 1024
   - 1024
-  sum: '4.972e+00'
+  sum: '4.941e+00'
 network.model.decoder.layers.6.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.633e-02'
-  mean: '3.225e-05'
-  min: '-4.407e-02'
+  device: cpu
+  max: '4.635e-02'
+  mean: '3.104e-05'
+  min: '-4.405e-02'
   shape:
   - 1024
-  sum: '3.303e-02'
+  sum: '3.179e-02'
 network.model.decoder.layers.6.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.147e-01'
-  mean: '4.657e-05'
+  mean: '4.645e-05'
   min: '-1.19e-01'
   shape:
   - 1024
   - 1024
-  sum: '4.883e+01'
+  sum: '4.871e+01'
 network.model.decoder.layers.6.self_attn_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
-  mean: '-1.389e-06'
+  mean: '-8.435e-07'
   min: '-1.257e-01'
   shape:
   - 1024
-  sum: '-1.423e-03'
+  sum: '-8.637e-04'
 network.model.decoder.layers.6.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.7.fc1.bias:
-  device: cuda:0
-  max: '1.077e-01'
+  device: cpu
+  max: '1.076e-01'
   mean: '-2.155e-02'
   min: '-1.226e-01'
   shape:
   - 4096
   sum: '-8.828e+01'
 network.model.decoder.layers.7.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.284e-01'
   mean: '1.858e-04'
   min: '-1.311e-01'
   shape:
   - 4096
   - 1024
-  sum: '7.793e+02'
+  sum: '7.794e+02'
 network.model.decoder.layers.7.fc2.bias:
-  device: cuda:0
-  max: '6.897e-02'
-  mean: '4.677e-05'
-  min: '-1.25e-01'
+  device: cpu
+  max: '6.895e-02'
+  mean: '4.630e-05'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '4.789e-02'
+  sum: '4.741e-02'
 network.model.decoder.layers.7.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.459e-01'
-  mean: '-4.578e-07'
-  min: '-2.5e-01'
+  mean: '-4.528e-07'
+  min: '-2.500e-01'
   shape:
   - 1024
   - 4096
-  sum: '-1.92e+00'
+  sum: '-1.899e+00'
 network.model.decoder.layers.7.final_layer_norm.bias:
-  device: cuda:0
+  device: cpu
   max: '1.093e-01'
-  mean: '-1.554e-03'
-  min: '-1.25e-01'
+  mean: '-1.555e-03'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '-1.591e+00'
+  sum: '-1.592e+00'
 network.model.decoder.layers.7.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.7.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.021e-01'
   mean: '1.303e-02'
   min: '-6.25e-02'
@@ -2898,133 +2898,133 @@ network.model.decoder.layers.7.self_attn.k_proj.bias:
   - 1024
   sum: '1.334e+01'
 network.model.decoder.layers.7.self_attn.k_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.323e-01'
-  mean: '1.285e-05'
+  mean: '1.288e-05'
   min: '-1.333e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.348e+01'
+  sum: '1.351e+01'
 network.model.decoder.layers.7.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '5.948e-02'
+  device: cpu
+  max: '5.946e-02'
   mean: '2.333e-04'
   min: '-1.25e-01'
   shape:
   - 1024
   sum: '2.389e-01'
 network.model.decoder.layers.7.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.316e-01'
-  mean: '-1.173e-06'
+  mean: '-1.180e-06'
   min: '-1.301e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.230e+00'
+  sum: '-1.238e+00'
 network.model.decoder.layers.7.self_attn.q_proj.bias:
-  device: cuda:0
-  max: '1.252e-01'
+  device: cpu
+  max: '1.253e-01'
   mean: '3.876e-03'
   min: '-1.261e-01'
   shape:
   - 1024
   sum: '3.969e+00'
 network.model.decoder.layers.7.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.272e-01'
-  mean: '-3.278e-06'
+  mean: '-3.281e-06'
   min: '-1.292e-01'
   shape:
   - 1024
   - 1024
-  sum: '-3.437e+00'
+  sum: '-3.441e+00'
 network.model.decoder.layers.7.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.297e-02'
-  mean: '4.138e-04'
-  min: '-4.077e-02'
+  device: cpu
+  max: '4.295e-02'
+  mean: '4.135e-04'
+  min: '-4.079e-02'
   shape:
   - 1024
-  sum: '4.237e-01'
+  sum: '4.234e-01'
 network.model.decoder.layers.7.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.183e-01'
-  mean: '-3.309e-05'
-  min: '-1.174e-01'
+  mean: '-3.315e-05'
+  min: '-1.175e-01'
   shape:
   - 1024
   - 1024
-  sum: '-3.47e+01'
+  sum: '-3.476e+01'
 network.model.decoder.layers.7.self_attn_layer_norm.bias:
-  device: cuda:0
-  max: '1.25e-01'
-  mean: '1.830e-04'
+  device: cpu
+  max: '1.250e-01'
+  mean: '1.825e-04'
   min: '-1.267e-01'
   shape:
   - 1024
-  sum: '1.874e-01'
+  sum: '1.869e-01'
 network.model.decoder.layers.7.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.8.fc1.bias:
-  device: cuda:0
-  max: '6.335e-02'
+  device: cpu
+  max: '6.337e-02'
   mean: '-2.258e-02'
   min: '-1.26e-01'
   shape:
   - 4096
   sum: '-9.249e+01'
 network.model.decoder.layers.8.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.278e-01'
-  mean: '5.06e-05'
+  mean: '5.059e-05'
   min: '-1.271e-01'
   shape:
   - 4096
   - 1024
   sum: '2.122e+02'
 network.model.decoder.layers.8.fc2.bias:
-  device: cuda:0
-  max: '6.818e-02'
-  mean: '-1.369e-04'
+  device: cpu
+  max: '6.816e-02'
+  mean: '-1.372e-04'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '-1.402e-01'
+  sum: '-1.405e-01'
 network.model.decoder.layers.8.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.392e-01'
-  mean: '-4.149e-06'
-  min: '-2.5e-01'
+  mean: '-4.206e-06'
+  min: '-2.500e-01'
   shape:
   - 1024
   - 4096
-  sum: '-1.740e+01'
+  sum: '-1.764e+01'
 network.model.decoder.layers.8.final_layer_norm.bias:
-  device: cuda:0
-  max: '6.47e-02'
+  device: cpu
+  max: '6.468e-02'
   mean: '-3.244e-03'
-  min: '-1.252e-01'
+  min: '-1.253e-01'
   shape:
   - 1024
   sum: '-3.322e+00'
 network.model.decoder.layers.8.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.8.self_attn.k_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '9.65e-02'
   mean: '1.109e-02'
   min: '-6.247e-02'
@@ -3032,167 +3032,167 @@ network.model.decoder.layers.8.self_attn.k_proj.bias:
   - 1024
   sum: '1.136e+01'
 network.model.decoder.layers.8.self_attn.k_proj.weight:
-  device: cuda:0
-  max: '1.318e-01'
-  mean: '8.991e-06'
+  device: cpu
+  max: '1.319e-01'
+  mean: '8.989e-06'
   min: '-1.32e-01'
   shape:
   - 1024
   - 1024
-  sum: '9.428e+00'
+  sum: '9.426e+00'
 network.model.decoder.layers.8.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '6.317e-02'
-  mean: '-7.463e-05'
+  device: cpu
+  max: '6.319e-02'
+  mean: '-7.502e-05'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '-7.643e-02'
+  sum: '-7.683e-02'
 network.model.decoder.layers.8.self_attn.out_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.306e-01'
   mean: '6.679e-06'
   min: '-1.327e-01'
   shape:
   - 1024
   - 1024
-  sum: '7.003e+00'
+  sum: '7.004e+00'
 network.model.decoder.layers.8.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.256e-01'
-  mean: '1.131e-05'
-  min: '-1.257e-01'
+  mean: '1.064e-05'
+  min: '-1.258e-01'
   shape:
   - 1024
-  sum: '1.159e-02'
+  sum: '1.09e-02'
 network.model.decoder.layers.8.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.311e-01'
-  mean: '-4.181e-07'
+  mean: '-4.081e-07'
   min: '-1.293e-01'
   shape:
   - 1024
   - 1024
-  sum: '-4.384e-01'
+  sum: '-4.279e-01'
 network.model.decoder.layers.8.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '4.486e-02'
-  mean: '5.294e-04'
-  min: '-4.657e-02'
+  device: cpu
+  max: '4.484e-02'
+  mean: '5.292e-04'
+  min: '-4.659e-02'
   shape:
   - 1024
-  sum: '5.421e-01'
+  sum: '5.419e-01'
 network.model.decoder.layers.8.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.242e-01'
-  mean: '1.489e-05'
+  mean: '1.485e-05'
   min: '-1.243e-01'
   shape:
   - 1024
   - 1024
-  sum: '1.561e+01'
+  sum: '1.557e+01'
 network.model.decoder.layers.8.self_attn_layer_norm.bias:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '1.027e-03'
-  min: '-1.254e-01'
+  min: '-1.253e-01'
   shape:
   - 1024
   sum: '1.052e+00'
 network.model.decoder.layers.8.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.9.fc1.bias:
-  device: cuda:0
-  max: '7.355e-02'
+  device: cpu
+  max: '7.357e-02'
   mean: '-2.086e-02'
-  min: '-8.301e-02'
+  min: '-8.303e-02'
   shape:
   - 4096
   sum: '-8.545e+01'
 network.model.decoder.layers.9.fc1.weight:
-  device: cuda:0
+  device: cpu
   max: '1.256e-01'
-  mean: '2.51e-05'
+  mean: '2.513e-05'
   min: '-1.265e-01'
   shape:
   - 4096
   - 1024
-  sum: '1.053e+02'
+  sum: '1.054e+02'
 network.model.decoder.layers.9.fc2.bias:
-  device: cuda:0
-  max: '6.647e-02'
-  mean: '2.622e-04'
-  min: '-1.25e-01'
+  device: cpu
+  max: '6.645e-02'
+  mean: '2.619e-04'
+  min: '-1.250e-01'
   shape:
   - 1024
-  sum: '2.685e-01'
+  sum: '2.682e-01'
 network.model.decoder.layers.9.fc2.weight:
-  device: cuda:0
+  device: cpu
   max: '1.256e-01'
-  mean: '-3.312e-06'
+  mean: '-3.337e-06'
   min: '-2.5e-01'
   shape:
   - 1024
   - 4096
-  sum: '-1.389e+01'
+  sum: '-1.4e+01'
 network.model.decoder.layers.9.final_layer_norm.bias:
-  device: cuda:0
-  max: '7.349e-02'
-  mean: '-8.035e-03'
+  device: cpu
+  max: '7.347e-02'
+  mean: '-8.034e-03'
   min: '-1.25e-01'
   shape:
   - 1024
   sum: '-8.227e+00'
 network.model.decoder.layers.9.final_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
-  mean: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
+  mean: '1.000e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.layers.9.self_attn.k_proj.bias:
-  device: cuda:0
-  max: '1.25e-01'
+  device: cpu
+  max: '1.250e-01'
   mean: '8.960e-03'
   min: '-1.25e-01'
   shape:
   - 1024
   sum: '9.175e+00'
 network.model.decoder.layers.9.self_attn.k_proj.weight:
-  device: cuda:0
-  max: '1.346e-01'
-  mean: '4.302e-05'
-  min: '-1.346e-01'
+  device: cpu
+  max: '1.347e-01'
+  mean: '4.305e-05'
+  min: '-1.347e-01'
   shape:
   - 1024
   - 1024
-  sum: '4.511e+01'
+  sum: '4.514e+01'
 network.model.decoder.layers.9.self_attn.out_proj.bias:
-  device: cuda:0
-  max: '6.616e-02'
-  mean: '-8.681e-05'
+  device: cpu
+  max: '6.614e-02'
+  mean: '-8.748e-05'
   min: '-1.25e-01'
   shape:
   - 1024
-  sum: '-8.89e-02'
+  sum: '-8.958e-02'
 network.model.decoder.layers.9.self_attn.out_proj.weight:
-  device: cuda:0
-  max: '1.497e-01'
-  mean: '-7.002e-06'
+  device: cpu
+  max: '1.496e-01'
+  mean: '-7.005e-06'
   min: '-1.382e-01'
   shape:
   - 1024
   - 1024
-  sum: '-7.342e+00'
+  sum: '-7.346e+00'
 network.model.decoder.layers.9.self_attn.q_proj.bias:
-  device: cuda:0
+  device: cpu
   max: '1.25e-01'
   mean: '2.336e-03'
   min: '-1.208e-01'
@@ -3200,60 +3200,60 @@ network.model.decoder.layers.9.self_attn.q_proj.bias:
   - 1024
   sum: '2.392e+00'
 network.model.decoder.layers.9.self_attn.q_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.344e-01'
-  mean: '-1.583e-05'
-  min: '-1.379e-01'
+  mean: '-1.582e-05'
+  min: '-1.38e-01'
   shape:
   - 1024
   - 1024
-  sum: '-1.66e+01'
+  sum: '-1.659e+01'
 network.model.decoder.layers.9.self_attn.v_proj.bias:
-  device: cuda:0
-  max: '6.241e-02'
-  mean: '2.777e-04'
-  min: '-6.464e-02'
+  device: cpu
+  max: '6.243e-02'
+  mean: '2.786e-04'
+  min: '-6.462e-02'
   shape:
   - 1024
-  sum: '2.844e-01'
+  sum: '2.853e-01'
 network.model.decoder.layers.9.self_attn.v_proj.weight:
-  device: cuda:0
+  device: cpu
   max: '1.131e-01'
   mean: '-2.935e-05'
   min: '-1.183e-01'
   shape:
   - 1024
   - 1024
-  sum: '-3.077e+01'
+  sum: '-3.078e+01'
 network.model.decoder.layers.9.self_attn_layer_norm.bias:
-  device: cuda:0
-  max: '7.812e-02'
-  mean: '9.632e-04'
+  device: cpu
+  max: '7.811e-02'
+  mean: '9.625e-04'
   min: '-1.255e-01'
   shape:
   - 1024
-  sum: '9.864e-01'
+  sum: '9.856e-01'
 network.model.decoder.layers.9.self_attn_layer_norm.weight:
-  device: cuda:0
-  max: '1.e+00'
+  device: cpu
+  max: '1.000e+00'
   mean: '1.e+00'
   min: '1.e+00'
   shape:
   - 1024
   sum: '1.024e+03'
 network.model.decoder.project_in.weight:
-  device: cuda:0
+  device: cpu
   max: '1.305e-01'
   mean: '3.482e-05'
   min: '-1.318e-01'
   shape:
   - 1024
   - 512
-  sum: '1.826e+01'
+  sum: '1.825e+01'
 network.model.decoder.project_out.weight:
-  device: cuda:0
+  device: cpu
   max: '1.373e-01'
-  mean: '8.706e-05'
+  mean: '8.704e-05'
   min: '-1.376e-01'
   shape:
   - 512
diff --git a/.regression_files/project/algorithms/llm_finetuning_test/test_training_batch_doesnt_change/llm_finetuning.yaml b/.regression_files/project/algorithms/llm_finetuning_test/test_training_batch_doesnt_change/llm_finetuning.yaml
deleted file mode 100644
index 84eb1516..00000000
--- a/.regression_files/project/algorithms/llm_finetuning_test/test_training_batch_doesnt_change/llm_finetuning.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-attention_mask:
-  device: cuda:0
-  max: 1
-  mean: '1.e+00'
-  min: 1
-  shape:
-  - 8
-  - 256
-  sum: 2048
-input_ids:
-  device: cuda:0
-  max: 50118
-  mean: '5.447e+03'
-  min: 2
-  shape:
-  - 8
-  - 256
-  sum: 11154886
-labels:
-  device: cuda:0
-  max: 50118
-  mean: '5.447e+03'
-  min: 2
-  shape:
-  - 8
-  - 256
-  sum: 11154886