/* Copyright 2018 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.
=======================================================================*/

// This class has been generated, DO NOT EDIT!

package org.tensorflow.op.core;

import org.tensorflow.Operand;
import org.tensorflow.Operation;
import org.tensorflow.OperationBuilder;
import org.tensorflow.Output;
import org.tensorflow.op.RawOp;
import org.tensorflow.op.Scope;
import org.tensorflow.op.annotation.Endpoint;
import org.tensorflow.op.annotation.Operator;
import org.tensorflow.types.family.TNumber;
import org.tensorflow.types.family.TType;

/**
 * Pads a tensor.
 * <p>
 * This operation pads `input` according to the `paddings` and `constant_values`
 * you specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is
 * the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates
 * how many padding values to add before the contents of `input` in that dimension,
 * and `paddings[D, 1]` indicates how many padding values to add after the contents
 * of `input` in that dimension. `constant_values` is a scalar tensor of the same
 * type as `input` that indicates the value to use for padding `input`.
 * <p>
 * The padded size of each dimension D of the output is:
 * <p>
 * `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`
 * <p>
 * For example:
 * <pre>{@code
 * # 't' is [[1, 1], [2, 2]]
 * # 'paddings' is [[1, 1], [2, 2]]
 * # 'constant_values' is 0
 * # rank of 't' is 2
 * pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
 *                       [0, 0, 1, 1, 0, 0]
 *                       [0, 0, 2, 2, 0, 0]
 *                       [0, 0, 0, 0, 0, 0]]
 * }</pre>
 * 
 * 
 * @param <T> data type for {@code output()} output
 */
@Operator
public final class Pad<T extends TType> extends RawOp implements Operand<T> {
  
  /**
   * Factory method to create a class wrapping a new Pad operation.
   * 
   * @param scope current scope
   * @param input 
   * @param paddings 
   * @param constantValues 
   * @return a new instance of Pad
   */
  @Endpoint(describeByClass = true)
  public static <T extends TType> Pad<T> create(Scope scope, Operand<T> input, Operand<? extends TNumber> paddings, Operand<T> constantValues) {
    OperationBuilder opBuilder = scope.env().opBuilder("PadV2", scope.makeOpName("Pad"));
    opBuilder.addInput(input.asOutput());
    opBuilder.addInput(paddings.asOutput());
    opBuilder.addInput(constantValues.asOutput());
    opBuilder = scope.apply(opBuilder);
    return new Pad<T>(opBuilder.build());
  }
  
  /**
   */
  public Output<T> output() {
    return output;
  }
  
  @Override
  public Output<T> asOutput() {
    return output;
  }
  
  /** The name of this op, as known by TensorFlow core engine */
  public static final String OP_NAME = "PadV2";
  
  private Output<T> output;
  
  private Pad(Operation operation) {
    super(operation);
    int outputIdx = 0;
    output = operation.output(outputIdx++);
  }
}
