/* 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.data;

import java.util.List;
import org.tensorflow.Operand;
import org.tensorflow.Operation;
import org.tensorflow.OperationBuilder;
import org.tensorflow.Output;
import org.tensorflow.ndarray.Shape;
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.TFloat32;
import org.tensorflow.types.TInt64;
import org.tensorflow.types.family.TType;

/**
 * Creates a dataset that takes a Bernoulli sample of the contents of another dataset.
 * <p>
 * There is no transformation in the `tf.data` Python API for creating this dataset.
 * Instead, it is created as a result of the `filter_with_random_uniform_fusion`
 * static optimization. Whether this optimization is performed is determined by the
 * `experimental_optimization.filter_with_random_uniform_fusion` option of
 * `tf.data.Options`.
 */
public final class SamplingDataset extends RawOp implements Operand<TType> {
  
  /**
   * Factory method to create a class wrapping a new SamplingDataset operation.
   * 
   * @param scope current scope
   * @param inputDataset 
   * @param rate A scalar representing the sample rate. Each element of `input_dataset` is
   * retained with this probability, independent of all other elements.
   * @param seed A scalar representing seed of random number generator.
   * @param seed2 A scalar representing seed2 of random number generator.
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of SamplingDataset
   */
  @Endpoint(describeByClass = true)
  public static SamplingDataset create(Scope scope, Operand<?> inputDataset, Operand<TFloat32> rate, Operand<TInt64> seed, Operand<TInt64> seed2, List<Class<? extends TType>> outputTypes, List<Shape> outputShapes) {
    OperationBuilder opBuilder = scope.env().opBuilder("SamplingDataset", scope.makeOpName("SamplingDataset"));
    opBuilder.addInput(inputDataset.asOutput());
    opBuilder.addInput(rate.asOutput());
    opBuilder.addInput(seed.asOutput());
    opBuilder.addInput(seed2.asOutput());
    opBuilder = scope.apply(opBuilder);
    opBuilder.setAttr("output_types", Operands.toDataTypes(outputTypes));
    Shape[] outputShapesArray = new Shape[outputShapes.size()];
    for (int i = 0; i < outputShapesArray.length; ++i) {
      outputShapesArray[i] = outputShapes.get(i);
    }
    opBuilder.setAttr("output_shapes", outputShapesArray);
    return new SamplingDataset(opBuilder.build());
  }
  
  /**
   */
  public Output<?> handle() {
    return handle;
  }
  
  @Override
  @SuppressWarnings("unchecked")
  public Output<TType> asOutput() {
    return (Output<TType>) handle;
  }
  
  /** The name of this op, as known by TensorFlow core engine */
  public static final String OP_NAME = "SamplingDataset";
  
  private Output<?> handle;
  
  private SamplingDataset(Operation operation) {
    super(operation);
    int outputIdx = 0;
    handle = operation.output(outputIdx++);
  }
}
