/* 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.random;

import org.tensorflow.Operand;
import org.tensorflow.Operation;
import org.tensorflow.OperationBuilder;
import org.tensorflow.Output;
import org.tensorflow.op.Operands;
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.TInt64;
import org.tensorflow.types.family.TNumber;

/**
 * Outputs deterministic pseudorandom random numbers from a binomial distribution.
 * <p>
 * Outputs random values from a binomial distribution.
 * <p>
 * The outputs are a deterministic function of `shape`, `seed`, `counts`, and `probs`.
 * 
 * @param <W> data type for {@code output()} output
 */
public final class StatelessRandomBinomial<W extends TNumber> extends RawOp implements Operand<W> {
  
  /**
   * Factory method to create a class wrapping a new StatelessRandomBinomial operation.
   * 
   * @param scope current scope
   * @param shape The shape of the output tensor.
   * @param seed 2 seeds (shape [2]).
   * @param counts The counts of the binomial distribution. Must be broadcastable with `probs`,
   * and broadcastable with the rightmost dimensions of `shape`.
   * @param probs The probability of success for the binomial distribution. Must be broadcastable
   * with `counts` and broadcastable with the rightmost dimensions of `shape`.
   * @param dtype The type of the output.
   * @return a new instance of StatelessRandomBinomial
   */
  @Endpoint(describeByClass = true)
  public static <W extends TNumber, V extends TNumber> StatelessRandomBinomial<W> create(Scope scope, Operand<? extends TNumber> shape, Operand<? extends TNumber> seed, Operand<V> counts, Operand<V> probs, Class<W> dtype) {
    OperationBuilder opBuilder = scope.env().opBuilder("StatelessRandomBinomial", scope.makeOpName("StatelessRandomBinomial"));
    opBuilder.addInput(shape.asOutput());
    opBuilder.addInput(seed.asOutput());
    opBuilder.addInput(counts.asOutput());
    opBuilder.addInput(probs.asOutput());
    opBuilder = scope.apply(opBuilder);
    opBuilder.setAttr("dtype", Operands.toDataType(dtype));
    return new StatelessRandomBinomial<W>(opBuilder.build());
  }
  
  /**
   * Factory method to create a class wrapping a new StatelessRandomBinomial operation using default output types.
   * 
   * @param scope current scope
   * @param shape The shape of the output tensor.
   * @param seed 2 seeds (shape [2]).
   * @param counts The counts of the binomial distribution. Must be broadcastable with `probs`,
   * and broadcastable with the rightmost dimensions of `shape`.
   * @param probs The probability of success for the binomial distribution. Must be broadcastable
   * with `counts` and broadcastable with the rightmost dimensions of `shape`.
   * @return a new instance of StatelessRandomBinomial
   */
  @Endpoint(describeByClass = true)
  public static <V extends TNumber> StatelessRandomBinomial<TInt64> create(Scope scope, Operand<? extends TNumber> shape, Operand<? extends TNumber> seed, Operand<V> counts, Operand<V> probs) {
    return create(scope, shape, seed, counts, probs, TInt64.class);
  }
  
  /**
   * Random values with specified shape.
   */
  public Output<W> output() {
    return output;
  }
  
  @Override
  public Output<W> asOutput() {
    return output;
  }
  
  /** The name of this op, as known by TensorFlow core engine */
  public static final String OP_NAME = "StatelessRandomBinomial";
  
  private Output<W> output;
  
  private StatelessRandomBinomial(Operation operation) {
    super(operation);
    int outputIdx = 0;
    output = operation.output(outputIdx++);
  }
}
