diff --git a/tensorflow-examples/src/main/java/org/tensorflow/model/examples/tensors/TensorCreation.java b/tensorflow-examples/src/main/java/org/tensorflow/model/examples/tensors/TensorCreation.java
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+/*
+ *  Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *  =======================================================================
+ */
+package org.tensorflow.model.examples.tensors;
+
+import org.tensorflow.Tensor;
+import org.tensorflow.tools.Shape;
+import org.tensorflow.tools.ndarray.IntNdArray;
+import org.tensorflow.tools.ndarray.NdArrays;
+import org.tensorflow.types.TInt32;
+
+import java.util.Arrays;
+
+/**
+ * Creates a few tensors of ranks: 0, 1, 2, 3.
+ */
+public class TensorCreation {
+    public static void main(String[] args) {
+        // Rank 0 Tensor
+        Tensor<TInt32> rank0Tensor = TInt32.scalarOf(42);
+
+        System.out.println("---- Scalar tensor ---------");
+
+        System.out.println("DataType: " + rank0Tensor.dataType().name());
+
+        System.out.println("Rank: " + rank0Tensor.shape().size());
+
+        System.out.println("Shape: " + Arrays.toString(rank0Tensor.shape().asArray()));
+
+        rank0Tensor.data().scalars().forEach(value -> System.out.println("Value: " + value.getObject()));
+
+        // Rank 1 Tensor
+        Tensor<TInt32> rank1Tensor = TInt32.vectorOf(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
+
+        System.out.println("---- Vector tensor ---------");
+
+        System.out.println("DataType: " + rank1Tensor.dataType().name());
+
+        System.out.println("Rank: " + rank1Tensor.shape().size());
+
+        System.out.println("Shape: " + Arrays.toString(rank1Tensor.shape().asArray()));
+
+        System.out.println("6th element: " + rank1Tensor.data().getInt(5));
+
+        // Rank 2 Tensor
+        // 3x2 matrix of ints.
+        IntNdArray matrix2d = NdArrays.ofInts(Shape.of(3, 2));
+
+        matrix2d.set(NdArrays.vectorOf(1, 2), 0)
+                .set(NdArrays.vectorOf(3, 4), 1)
+                .set(NdArrays.vectorOf(5, 6), 2);
+
+        Tensor<TInt32> rank2Tensor = TInt32.tensorOf(matrix2d);
+
+        System.out.println("---- Matrix tensor ---------");
+
+        System.out.println("DataType: " + rank2Tensor.dataType().name());
+
+        System.out.println("Rank: " + rank2Tensor.shape().size());
+
+        System.out.println("Shape: " + Arrays.toString(rank2Tensor.shape().asArray()));
+
+        System.out.println("6th element: " + rank2Tensor.data().getInt(2, 1));
+
+        // Rank 3 Tensor
+        // 3*2*4 matrix of ints.
+        IntNdArray matrix3d = NdArrays.ofInts(Shape.of(3, 2, 4));
+
+        matrix3d.elements(0).forEach(matrix -> {
+            matrix
+                    .set(NdArrays.vectorOf(1, 2, 3, 4), 0)
+                    .set(NdArrays.vectorOf(5, 6, 7, 8), 1);
+        });
+
+        Tensor<TInt32> rank3Tensor = TInt32.tensorOf(matrix3d);
+
+        System.out.println("---- Matrix tensor ---------");
+
+        System.out.println("DataType: " + rank3Tensor.dataType().name());
+
+        System.out.println("Rank: " + rank3Tensor.shape().size());
+
+        System.out.println("Shape: " + Arrays.toString(rank3Tensor.shape().asArray()));
+
+        System.out.println("n-th element: " + rank3Tensor.data().getInt(2, 1, 3));
+    }
+}