[tensoflow] Add truncated-normal operation

api/src/main/java/ai/djl/ndarray/BaseNDManager.java

@@ -153,6 +153,12 @@ public NDArray randomNormal(float loc, float scale, Shape shape, DataType dataTy
         throw new UnsupportedOperationException("Not supported!");
     }
 
+    /** {@inheritDoc} */
+    @Override
+    public NDArray truncatedNormal(float loc, float scale, Shape shape, DataType dataType) {
+        throw new UnsupportedOperationException("Not supported!");
+    }
+
     /** {@inheritDoc} */
     @Override
     public NDArray randomMultinomial(int n, NDArray pValues) {

api/src/main/java/ai/djl/ndarray/NDManager.java

@@ -1232,6 +1232,65 @@ default NDArray randomNormal(
         return newSubManager(device).randomNormal(loc, scale, shape, dataType);
     }
 
+    /**
+     * Draws random samples from a normal (Gaussian) distribution with mean 0 and standard deviation
+     * 1, discarding and re-drawing any samples that are more than two standard deviations from the
+     * mean.
+     *
+     * <p>Samples are distributed according to a normal distribution parametrized by mean = 0 and
+     * standard deviation = 1.
+     *
+     * @param shape the output {@link Shape}
+     * @return the drawn samples {@link NDArray}
+     */
+    default NDArray truncatedNormal(Shape shape) {
+        return truncatedNormal(0f, 1f, shape, DataType.FLOAT32);
+    }
+
+    /**
+     * Draws random samples from a normal (Gaussian) distribution with mean 0 and standard deviation
+     * 1, discarding and re-drawing any samples that are more than two standard deviations from the
+     * mean.
+     *
+     * @param shape the output {@link Shape}
+     * @param dataType the {@link DataType} of the {@link NDArray}
+     * @return the drawn samples {@link NDArray}
+     */
+    default NDArray truncatedNormal(Shape shape, DataType dataType) {
+        return truncatedNormal(0.0f, 1.0f, shape, dataType);
+    }
+
+    /**
+     * Draws random samples from a normal (Gaussian) distribution, discarding and re-drawing any
+     * samples that are more than two standard deviations from the mean.
+     *
+     * @param loc the mean (centre) of the distribution
+     * @param scale the standard deviation (spread or "width") of the distribution
+     * @param shape the output {@link Shape}
+     * @param dataType the {@link DataType} of the {@link NDArray}
+     * @return the drawn samples {@link NDArray}
+     */
+    NDArray truncatedNormal(float loc, float scale, Shape shape, DataType dataType);
+
+    /**
+     * Draws random samples from a normal (Gaussian) distribution, discarding and re-drawing any
+     * samples that are more than two standard deviations from the mean.
+     *
+     * @param loc the mean (centre) of the distribution
+     * @param scale the standard deviation (spread or "width") of the distribution
+     * @param shape the output {@link Shape}
+     * @param dataType the {@link DataType} of the {@link NDArray}
+     * @param device the {@link Device} of the {@link NDArray}
+     * @return the drawn samples {@link NDArray}
+     */
+    default NDArray truncatedNormal(
+            float loc, float scale, Shape shape, DataType dataType, Device device) {
+        if (device == null || device.equals(getDevice())) {
+            return truncatedNormal(loc, scale, shape, dataType);
+        }
+        return newSubManager(device).truncatedNormal(loc, scale, shape, dataType);
+    }
+
     /**
      * Draw samples from a multinomial distribution.
      *

tensorflow/tensorflow-engine/src/main/java/ai/djl/tensorflow/engine/TfNDManager.java

@@ -242,6 +242,31 @@ public NDArray randomNormal(float loc, float scale, Shape shape, DataType dataTy
         }
     }
 
+    /** {@inheritDoc} */
+    @Override
+    public NDArray truncatedNormal(float loc, float scale, Shape shape, DataType dataType) {
+        if (DataType.STRING.equals(dataType)) {
+            throw new IllegalArgumentException("String data type is not supported!");
+        }
+        NDArray axes = create(shape.getShape());
+        TfOpExecutor opBuilder =
+                opExecutor("TruncatedNormal").addInput(axes).addParam("dtype", dataType);
+        Integer seed = getEngine().getSeed();
+        if (seed != null) {
+            // seed1 is graph-level seed
+            // set it to default graph seed used by tensorflow
+            // https://github.com/tensorflow/tensorflow/blob/85c8b2a817f95a3e979ecd1ed95bff1dc1335cff/tensorflow/python/framework/random_seed.py#L31
+            opBuilder.addParam("seed", 87654321);
+            opBuilder.addParam("seed2", seed);
+        }
+        try (NDArray array = opBuilder.buildSingletonOrThrow();
+                NDArray temp = array.mul(scale)) {
+            return temp.add(loc);
+        } finally {
+            axes.close();
+        }
+    }
+
     /** {@inheritDoc} */
     @Override
     public TfNDManager newSubManager(Device device) {