Remove extra parameters from the TensorFlowInferenceModel#copy

TensorFlowInferenceModel does not have any layers, so it only makes sense to copy weights. And since the model class is not trainable, optimizer state does not need to be copied.

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/inference/TensorFlowInferenceModel.kt

@@ -147,14 +147,11 @@ public open class TensorFlowInferenceModel : InferenceModel {
     }
 
     override fun copy(): TensorFlowInferenceModel {
-        return copy(copiedModelName = null, saveOptimizerState = false, copyWeights = true)
+        return copy(copiedModelName = null)
     }
 
-    public fun copy(
-        copiedModelName: String? = null,
-        saveOptimizerState: Boolean = false, // TODO, check this case
-        copyWeights: Boolean = true
-    ): TensorFlowInferenceModel {
+    /** Returns a copy of this model. */
+    public fun copy(copiedModelName: String? = null): TensorFlowInferenceModel {
         val model = TensorFlowInferenceModel()
         model.kGraph = this.kGraph.copy()
         model.tf = Ops.create(model.kGraph.tfGraph)
@@ -163,25 +160,17 @@ public open class TensorFlowInferenceModel : InferenceModel {
         model.input = input
         model.output = output
         if (copiedModelName != null) model.name = name
-        // TODO: check that tensors are closed after usage
-        if (copyWeights) {
-            val modelWeightsExtractorRunner = session.runner()
-            val variableNames = kGraph.variableNames()
-            check(variableNames.isNotEmpty()) {
-                "Found 0 variable names in TensorFlow graph $kGraph. " +
-                        "If copied model has no weights, set flag `copyWeights` to `false`."
-            }
-
-            val variableNamesToCopy = variableNames.filter { variableName ->
-                saveOptimizerState || !isOptimizerVariable(variableName)
-            }
-            variableNamesToCopy.forEach(modelWeightsExtractorRunner::fetch)
-            val modelWeights = variableNamesToCopy.zip(modelWeightsExtractorRunner.run()).toMap()
 
+        val variableNames = kGraph.variableNames()
+        if (variableNames.isNotEmpty()) {
+            val modelWeightsExtractorRunner = session.runner()
+            variableNames.forEach(modelWeightsExtractorRunner::fetch)
+            val modelWeights = variableNames.zip(modelWeightsExtractorRunner.run()).toMap()
             model.loadVariables(modelWeights.keys) { variableName, _ ->
                 modelWeights[variableName]!!.use { it.convertTensorToMultiDimArray() }
             }
         }
+
         model.isModelInitialized = true
         return model
     }