/* 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.math;

import java.util.Arrays;
import org.tensorflow.GraphOperation;
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.RawOpInputs;
import org.tensorflow.op.Scope;
import org.tensorflow.op.annotation.Endpoint;
import org.tensorflow.op.annotation.OpInputsMetadata;
import org.tensorflow.op.annotation.OpMetadata;
import org.tensorflow.op.annotation.Operator;
import org.tensorflow.proto.framework.DataType;
import org.tensorflow.types.family.TType;

/**
 * Returns 0 if the denominator is zero.
 * <em>NOTE</em>: {@code math.DivNoNan} supports broadcasting. More about broadcasting
 *  <a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a>
 *
 * @param <T> data type for {@code z} output
 */
@OpMetadata(
    opType = DivNoNan.OP_NAME,
    inputsClass = DivNoNan.Inputs.class
)
@Operator(
    group = "math"
)
public final class DivNoNan<T extends TType> extends RawOp implements Operand<T> {
  /**
   * The name of this op, as known by TensorFlow core engine
   */
  public static final String OP_NAME = "DivNoNan";

  private Output<T> z;

  public DivNoNan(Operation operation) {
    super(operation, OP_NAME);
    int outputIdx = 0;
    z = operation.output(outputIdx++);
  }

  /**
   * Factory method to create a class wrapping a new DivNoNan operation.
   *
   * @param scope current scope
   * @param x The x value
   * @param y The y value
   * @param <T> data type for {@code DivNoNan} output and operands
   * @return a new instance of DivNoNan
   */
  @Endpoint(
      describeByClass = true
  )
  public static <T extends TType> DivNoNan<T> create(Scope scope, Operand<T> x, Operand<T> y) {
    OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "DivNoNan");
    opBuilder.addInput(x.asOutput());
    opBuilder.addInput(y.asOutput());
    return new DivNoNan<>(opBuilder.build());
  }

  /**
   * Gets z.
   *
   * @return z.
   */
  public Output<T> z() {
    return z;
  }

  @Override
  public Output<T> asOutput() {
    return z;
  }

  @OpInputsMetadata(
      outputsClass = DivNoNan.class
  )
  public static class Inputs<T extends TType> extends RawOpInputs<DivNoNan<T>> {
    /**
     * The x input
     */
    public final Operand<T> x;

    /**
     * The y input
     */
    public final Operand<T> y;

    /**
     * The T attribute
     */
    public final DataType T;

    public Inputs(GraphOperation op) {
      super(new DivNoNan<>(op), op, Arrays.asList("T"));
      int inputIndex = 0;
      x = (Operand<T>) op.input(inputIndex++);
      y = (Operand<T>) op.input(inputIndex++);
      T = op.attributes().getAttrType("T");
    }
  }
}
