EVOLUTION-MANAGER

Edit File: candidate_sampling_ops.h

// This file is MACHINE GENERATED! Do not edit.

#ifndef TENSORFLOW_CC_OPS_CANDIDATE_SAMPLING_OPS_H_
#define TENSORFLOW_CC_OPS_CANDIDATE_SAMPLING_OPS_H_

// This file is MACHINE GENERATED! Do not edit.

#include "tensorflow/cc/framework/ops.h"
#include "tensorflow/cc/framework/scope.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/gtl/array_slice.h"

namespace tensorflow {
namespace ops {

/// @defgroup candidate_sampling_ops Candidate Sampling Ops
/// @{

/// Generates labels for candidate sampling with a learned unigram distribution.
///
/// See explanations of candidate sampling and the data formats at
/// go/candidate-sampling.
///
/// For each batch, this op picks a single set of sampled candidate labels.
///
/// The advantages of sampling candidates per-batch are simplicity and the
/// possibility of efficient dense matrix multiplication. The disadvantage is that
/// the sampled candidates must be chosen independently of the context and of the
/// true labels.
///
/// Arguments:
/// * scope: A Scope object
/// * true_classes: A batch_size * num_true matrix, in which each row contains the
/// IDs of the num_true target_classes in the corresponding original label.
/// * num_true: Number of true labels per context.
/// * num_sampled: Number of candidates to produce.
/// * unique: If unique is true, we sample with rejection, so that all sampled
/// candidates in a batch are unique. This requires some approximation to
/// estimate the post-rejection sampling probabilities.
///
/// Optional attributes (see `Attrs`):
/// * seed: If either seed or seed2 are set to be non-zero, the random number
/// generator is seeded by the given seed.  Otherwise, it is seeded by a
/// random seed.
/// * seed2: An second seed to avoid seed collision.
///
/// Returns:
/// * `Output` sampled_candidates: A vector of length num_sampled, in which each element is
/// the ID of a sampled candidate.
/// * `Output` true_expected_count: A batch_size * num_true matrix, representing
/// the number of times each candidate is expected to occur in a batch
/// of sampled candidates. If unique=true, then this is a probability.
/// * `Output` sampled_expected_count: A vector of length num_sampled, for each sampled
/// candidate representing the number of times the candidate is expected
/// to occur in a batch of sampled candidates.  If unique=true, then this is a
/// probability.
class AllCandidateSampler {
 public:
  /// Optional attribute setters for AllCandidateSampler
  struct Attrs {
    /// If either seed or seed2 are set to be non-zero, the random number
    /// generator is seeded by the given seed.  Otherwise, it is seeded by a
    /// random seed.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Seed(int64 x) {
      Attrs ret = *this;
      ret.seed_ = x;
      return ret;
    }

/// An second seed to avoid seed collision.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Seed2(int64 x) {
      Attrs ret = *this;
      ret.seed2_ = x;
      return ret;
    }

int64 seed_ = 0;
    int64 seed2_ = 0;
  };
  AllCandidateSampler(const ::tensorflow::Scope& scope, ::tensorflow::Input
                    true_classes, int64 num_true, int64 num_sampled, bool
                    unique);
  AllCandidateSampler(const ::tensorflow::Scope& scope, ::tensorflow::Input
                    true_classes, int64 num_true, int64 num_sampled, bool
                    unique, const AllCandidateSampler::Attrs& attrs);

static Attrs Seed(int64 x) {
    return Attrs().Seed(x);
  }
  static Attrs Seed2(int64 x) {
    return Attrs().Seed2(x);
  }

Operation operation;
  ::tensorflow::Output sampled_candidates;
  ::tensorflow::Output true_expected_count;
  ::tensorflow::Output sampled_expected_count;
};

/// Computes the ids of the positions in sampled_candidates that match true_labels.
///
/// When doing log-odds NCE, the result of this op should be passed through a
/// SparseToDense op, then added to the logits of the sampled candidates. This has
/// the effect of 'removing' the sampled labels that match the true labels by
/// making the classifier sure that they are sampled labels.
///
/// Arguments:
/// * scope: A Scope object
/// * true_classes: The true_classes output of UnpackSparseLabels.
/// * sampled_candidates: The sampled_candidates output of CandidateSampler.
/// * num_true: Number of true labels per context.
///
/// Optional attributes (see `Attrs`):
/// * seed: If either seed or seed2 are set to be non-zero, the random number
/// generator is seeded by the given seed.  Otherwise, it is seeded by a
/// random seed.
/// * seed2: An second seed to avoid seed collision.
///
/// Returns:
/// * `Output` indices: A vector of indices corresponding to rows of true_candidates.
/// * `Output` ids: A vector of IDs of positions in sampled_candidates that match a true_label
/// for the row with the corresponding index in indices.
/// * `Output` weights: A vector of the same length as indices and ids, in which each element
/// is -FLOAT_MAX.
class ComputeAccidentalHits {
 public:
  /// Optional attribute setters for ComputeAccidentalHits
  struct Attrs {
    /// If either seed or seed2 are set to be non-zero, the random number
    /// generator is seeded by the given seed.  Otherwise, it is seeded by a
    /// random seed.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Seed(int64 x) {
      Attrs ret = *this;
      ret.seed_ = x;
      return ret;
    }

/// An second seed to avoid seed collision.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Seed2(int64 x) {
      Attrs ret = *this;
      ret.seed2_ = x;
      return ret;
    }

int64 seed_ = 0;
    int64 seed2_ = 0;
  };
  ComputeAccidentalHits(const ::tensorflow::Scope& scope, ::tensorflow::Input
                      true_classes, ::tensorflow::Input sampled_candidates,
                      int64 num_true);
  ComputeAccidentalHits(const ::tensorflow::Scope& scope, ::tensorflow::Input
                      true_classes, ::tensorflow::Input sampled_candidates,
                      int64 num_true, const ComputeAccidentalHits::Attrs&
                      attrs);

static Attrs Seed(int64 x) {
    return Attrs().Seed(x);
  }
  static Attrs Seed2(int64 x) {
    return Attrs().Seed2(x);
  }

Operation operation;
  ::tensorflow::Output indices;
  ::tensorflow::Output ids;
  ::tensorflow::Output weights;
};

/// Generates labels for candidate sampling with a learned unigram distribution.
///
/// A unigram sampler could use a fixed unigram distribution read from a
/// file or passed in as an in-memory array instead of building up the distribution
/// from data on the fly. There is also an option to skew the distribution by
/// applying a distortion power to the weights.
///
/// The vocabulary file should be in CSV-like format, with the last field
/// being the weight associated with the word.
///
/// For each batch, this op picks a single set of sampled candidate labels.
///
/// The advantages of sampling candidates per-batch are simplicity and the
/// possibility of efficient dense matrix multiplication. The disadvantage is that
/// the sampled candidates must be chosen independently of the context and of the
/// true labels.
///
/// Arguments:
/// * scope: A Scope object
/// * true_classes: A batch_size * num_true matrix, in which each row contains the
/// IDs of the num_true target_classes in the corresponding original label.
/// * num_true: Number of true labels per context.
/// * num_sampled: Number of candidates to randomly sample.
/// * unique: If unique is true, we sample with rejection, so that all sampled
/// candidates in a batch are unique. This requires some approximation to
/// estimate the post-rejection sampling probabilities.
/// * range_max: The sampler will sample integers from the interval [0, range_max).
///
/// Optional attributes (see `Attrs`):
/// * vocab_file: Each valid line in this file (which should have a CSV-like format)
/// corresponds to a valid word ID. IDs are in sequential order, starting from
/// num_reserved_ids. The last entry in each line is expected to be a value
/// corresponding to the count or relative probability. Exactly one of vocab_file
/// and unigrams needs to be passed to this op.
/// * distortion: The distortion is used to skew the unigram probability distribution.
/// Each weight is first raised to the distortion's power before adding to the
/// internal unigram distribution. As a result, distortion = 1.0 gives regular
/// unigram sampling (as defined by the vocab file), and distortion = 0.0 gives
/// a uniform distribution.
/// * num_reserved_ids: Optionally some reserved IDs can be added in the range [0,
/// ..., num_reserved_ids) by the users. One use case is that a special unknown
/// word token is used as ID 0. These IDs will have a sampling probability of 0.
/// * num_shards: A sampler can be used to sample from a subset of the original range
/// in order to speed up the whole computation through parallelism. This parameter
/// (together with 'shard') indicates the number of partitions that are being
/// used in the overall computation.
/// * shard: A sampler can be used to sample from a subset of the original range
/// in order to speed up the whole computation through parallelism. This parameter
/// (together with 'num_shards') indicates the particular partition number of a
/// sampler op, when partitioning is being used.
/// * unigrams: A list of unigram counts or probabilities, one per ID in sequential
/// order. Exactly one of vocab_file and unigrams should be passed to this op.
/// * seed: If either seed or seed2 are set to be non-zero, the random number
/// generator is seeded by the given seed.  Otherwise, it is seeded by a
/// random seed.
/// * seed2: An second seed to avoid seed collision.
///
/// Returns:
/// * `Output` sampled_candidates: A vector of length num_sampled, in which each element is
/// the ID of a sampled candidate.
/// * `Output` true_expected_count: A batch_size * num_true matrix, representing
/// the number of times each candidate is expected to occur in a batch
/// of sampled candidates. If unique=true, then this is a probability.
/// * `Output` sampled_expected_count: A vector of length num_sampled, for each sampled
/// candidate representing the number of times the candidate is expected
/// to occur in a batch of sampled candidates.  If unique=true, then this is a
/// probability.
class FixedUnigramCandidateSampler {
 public:
  /// Optional attribute setters for FixedUnigramCandidateSampler
  struct Attrs {
    /// Each valid line in this file (which should have a CSV-like format)
    /// corresponds to a valid word ID. IDs are in sequential order, starting from
    /// num_reserved_ids. The last entry in each line is expected to be a value
    /// corresponding to the count or relative probability. Exactly one of vocab_file
    /// and unigrams needs to be passed to this op.
    ///
    /// Defaults to ""
    TF_MUST_USE_RESULT Attrs VocabFile(StringPiece x) {
      Attrs ret = *this;
      ret.vocab_file_ = x;
      return ret;
    }

/// The distortion is used to skew the unigram probability distribution.
    /// Each weight is first raised to the distortion's power before adding to the
    /// internal unigram distribution. As a result, distortion = 1.0 gives regular
    /// unigram sampling (as defined by the vocab file), and distortion = 0.0 gives
    /// a uniform distribution.
    ///
    /// Defaults to 1
    TF_MUST_USE_RESULT Attrs Distortion(float x) {
      Attrs ret = *this;
      ret.distortion_ = x;
      return ret;
    }

/// Optionally some reserved IDs can be added in the range [0,
    /// ..., num_reserved_ids) by the users. One use case is that a special unknown
    /// word token is used as ID 0. These IDs will have a sampling probability of 0.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs NumReservedIds(int64 x) {
      Attrs ret = *this;
      ret.num_reserved_ids_ = x;
      return ret;
    }

/// A sampler can be used to sample from a subset of the original range
    /// in order to speed up the whole computation through parallelism. This parameter
    /// (together with 'shard') indicates the number of partitions that are being
    /// used in the overall computation.
    ///
    /// Defaults to 1
    TF_MUST_USE_RESULT Attrs NumShards(int64 x) {
      Attrs ret = *this;
      ret.num_shards_ = x;
      return ret;
    }

/// A sampler can be used to sample from a subset of the original range
    /// in order to speed up the whole computation through parallelism. This parameter
    /// (together with 'num_shards') indicates the particular partition number of a
    /// sampler op, when partitioning is being used.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Shard(int64 x) {
      Attrs ret = *this;
      ret.shard_ = x;
      return ret;
    }

/// A list of unigram counts or probabilities, one per ID in sequential
    /// order. Exactly one of vocab_file and unigrams should be passed to this op.
    ///
    /// Defaults to []
    TF_MUST_USE_RESULT Attrs Unigrams(const gtl::ArraySlice<float>& x) {
      Attrs ret = *this;
      ret.unigrams_ = x;
      return ret;
    }

/// If either seed or seed2 are set to be non-zero, the random number
    /// generator is seeded by the given seed.  Otherwise, it is seeded by a
    /// random seed.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Seed(int64 x) {
      Attrs ret = *this;
      ret.seed_ = x;
      return ret;
    }

/// An second seed to avoid seed collision.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Seed2(int64 x) {
      Attrs ret = *this;
      ret.seed2_ = x;
      return ret;
    }

StringPiece vocab_file_ = "";
    float distortion_ = 1.0f;
    int64 num_reserved_ids_ = 0;
    int64 num_shards_ = 1;
    int64 shard_ = 0;
    gtl::ArraySlice<float> unigrams_ = {};
    int64 seed_ = 0;
    int64 seed2_ = 0;
  };
  FixedUnigramCandidateSampler(const ::tensorflow::Scope& scope,
                             ::tensorflow::Input true_classes, int64 num_true,
                             int64 num_sampled, bool unique, int64 range_max);
  FixedUnigramCandidateSampler(const ::tensorflow::Scope& scope,
                             ::tensorflow::Input true_classes, int64 num_true,
                             int64 num_sampled, bool unique, int64 range_max,
                             const FixedUnigramCandidateSampler::Attrs& attrs);

static Attrs VocabFile(StringPiece x) {
    return Attrs().VocabFile(x);
  }
  static Attrs Distortion(float x) {
    return Attrs().Distortion(x);
  }
  static Attrs NumReservedIds(int64 x) {
    return Attrs().NumReservedIds(x);
  }
  static Attrs NumShards(int64 x) {
    return Attrs().NumShards(x);
  }
  static Attrs Shard(int64 x) {
    return Attrs().Shard(x);
  }
  static Attrs Unigrams(const gtl::ArraySlice<float>& x) {
    return Attrs().Unigrams(x);
  }
  static Attrs Seed(int64 x) {
    return Attrs().Seed(x);
  }
  static Attrs Seed2(int64 x) {
    return Attrs().Seed2(x);
  }

Operation operation;
  ::tensorflow::Output sampled_candidates;
  ::tensorflow::Output true_expected_count;
  ::tensorflow::Output sampled_expected_count;
};

/// Generates labels for candidate sampling with a learned unigram distribution.
///
/// See explanations of candidate sampling and the data formats at
/// go/candidate-sampling.
///
/// For each batch, this op picks a single set of sampled candidate labels.
///
/// The advantages of sampling candidates per-batch are simplicity and the
/// possibility of efficient dense matrix multiplication. The disadvantage is that
/// the sampled candidates must be chosen independently of the context and of the
/// true labels.
///
/// Arguments:
/// * scope: A Scope object
/// * true_classes: A batch_size * num_true matrix, in which each row contains the
/// IDs of the num_true target_classes in the corresponding original label.
/// * num_true: Number of true labels per context.
/// * num_sampled: Number of candidates to randomly sample.
/// * unique: If unique is true, we sample with rejection, so that all sampled
/// candidates in a batch are unique. This requires some approximation to
/// estimate the post-rejection sampling probabilities.
/// * range_max: The sampler will sample integers from the interval [0, range_max).
///
/// Optional attributes (see `Attrs`):
/// * seed: If either seed or seed2 are set to be non-zero, the random number
/// generator is seeded by the given seed.  Otherwise, it is seeded by a
/// random seed.
/// * seed2: An second seed to avoid seed collision.
///
/// Returns:
/// * `Output` sampled_candidates: A vector of length num_sampled, in which each element is
/// the ID of a sampled candidate.
/// * `Output` true_expected_count: A batch_size * num_true matrix, representing
/// the number of times each candidate is expected to occur in a batch
/// of sampled candidates. If unique=true, then this is a probability.
/// * `Output` sampled_expected_count: A vector of length num_sampled, for each sampled
/// candidate representing the number of times the candidate is expected
/// to occur in a batch of sampled candidates.  If unique=true, then this is a
/// probability.
class LearnedUnigramCandidateSampler {
 public:
  /// Optional attribute setters for LearnedUnigramCandidateSampler
  struct Attrs {
    /// If either seed or seed2 are set to be non-zero, the random number
    /// generator is seeded by the given seed.  Otherwise, it is seeded by a
    /// random seed.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Seed(int64 x) {
      Attrs ret = *this;
      ret.seed_ = x;
      return ret;
    }

/// An second seed to avoid seed collision.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Seed2(int64 x) {
      Attrs ret = *this;
      ret.seed2_ = x;
      return ret;
    }

int64 seed_ = 0;
    int64 seed2_ = 0;
  };
  LearnedUnigramCandidateSampler(const ::tensorflow::Scope& scope,
                               ::tensorflow::Input true_classes, int64
                               num_true, int64 num_sampled, bool unique, int64
                               range_max);
  LearnedUnigramCandidateSampler(const ::tensorflow::Scope& scope,
                               ::tensorflow::Input true_classes, int64
                               num_true, int64 num_sampled, bool unique, int64
                               range_max, const
                               LearnedUnigramCandidateSampler::Attrs& attrs);

static Attrs Seed(int64 x) {
    return Attrs().Seed(x);
  }
  static Attrs Seed2(int64 x) {
    return Attrs().Seed2(x);
  }

Operation operation;
  ::tensorflow::Output sampled_candidates;
  ::tensorflow::Output true_expected_count;
  ::tensorflow::Output sampled_expected_count;
};

/// Generates labels for candidate sampling with a log-uniform distribution.
///
/// See explanations of candidate sampling and the data formats at
/// go/candidate-sampling.
///
/// For each batch, this op picks a single set of sampled candidate labels.
///
/// The advantages of sampling candidates per-batch are simplicity and the
/// possibility of efficient dense matrix multiplication. The disadvantage is that
/// the sampled candidates must be chosen independently of the context and of the
/// true labels.
///
/// Arguments:
/// * scope: A Scope object
/// * true_classes: A batch_size * num_true matrix, in which each row contains the
/// IDs of the num_true target_classes in the corresponding original label.
/// * num_true: Number of true labels per context.
/// * num_sampled: Number of candidates to randomly sample.
/// * unique: If unique is true, we sample with rejection, so that all sampled
/// candidates in a batch are unique. This requires some approximation to
/// estimate the post-rejection sampling probabilities.
/// * range_max: The sampler will sample integers from the interval [0, range_max).
///
/// Optional attributes (see `Attrs`):
/// * seed: If either seed or seed2 are set to be non-zero, the random number
/// generator is seeded by the given seed.  Otherwise, it is seeded by a
/// random seed.
/// * seed2: An second seed to avoid seed collision.
///
/// Returns:
/// * `Output` sampled_candidates: A vector of length num_sampled, in which each element is
/// the ID of a sampled candidate.
/// * `Output` true_expected_count: A batch_size * num_true matrix, representing
/// the number of times each candidate is expected to occur in a batch
/// of sampled candidates. If unique=true, then this is a probability.
/// * `Output` sampled_expected_count: A vector of length num_sampled, for each sampled
/// candidate representing the number of times the candidate is expected
/// to occur in a batch of sampled candidates.  If unique=true, then this is a
/// probability.
class LogUniformCandidateSampler {
 public:
  /// Optional attribute setters for LogUniformCandidateSampler
  struct Attrs {
    /// If either seed or seed2 are set to be non-zero, the random number
    /// generator is seeded by the given seed.  Otherwise, it is seeded by a
    /// random seed.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Seed(int64 x) {
      Attrs ret = *this;
      ret.seed_ = x;
      return ret;
    }

/// An second seed to avoid seed collision.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Seed2(int64 x) {
      Attrs ret = *this;
      ret.seed2_ = x;
      return ret;
    }

int64 seed_ = 0;
    int64 seed2_ = 0;
  };
  LogUniformCandidateSampler(const ::tensorflow::Scope& scope,
                           ::tensorflow::Input true_classes, int64 num_true,
                           int64 num_sampled, bool unique, int64 range_max);
  LogUniformCandidateSampler(const ::tensorflow::Scope& scope,
                           ::tensorflow::Input true_classes, int64 num_true,
                           int64 num_sampled, bool unique, int64 range_max,
                           const LogUniformCandidateSampler::Attrs& attrs);

static Attrs Seed(int64 x) {
    return Attrs().Seed(x);
  }
  static Attrs Seed2(int64 x) {
    return Attrs().Seed2(x);
  }

Operation operation;
  ::tensorflow::Output sampled_candidates;
  ::tensorflow::Output true_expected_count;
  ::tensorflow::Output sampled_expected_count;
};

/// Generates labels for candidate sampling with a uniform distribution.
///
/// See explanations of candidate sampling and the data formats at
/// go/candidate-sampling.
///
/// For each batch, this op picks a single set of sampled candidate labels.
///
/// The advantages of sampling candidates per-batch are simplicity and the
/// possibility of efficient dense matrix multiplication. The disadvantage is that
/// the sampled candidates must be chosen independently of the context and of the
/// true labels.
///
/// Arguments:
/// * scope: A Scope object
/// * true_classes: A batch_size * num_true matrix, in which each row contains the
/// IDs of the num_true target_classes in the corresponding original label.
/// * num_true: Number of true labels per context.
/// * num_sampled: Number of candidates to randomly sample.
/// * unique: If unique is true, we sample with rejection, so that all sampled
/// candidates in a batch are unique. This requires some approximation to
/// estimate the post-rejection sampling probabilities.
/// * range_max: The sampler will sample integers from the interval [0, range_max).
///
/// Optional attributes (see `Attrs`):
/// * seed: If either seed or seed2 are set to be non-zero, the random number
/// generator is seeded by the given seed.  Otherwise, it is seeded by a
/// random seed.
/// * seed2: An second seed to avoid seed collision.
///
/// Returns:
/// * `Output` sampled_candidates: A vector of length num_sampled, in which each element is
/// the ID of a sampled candidate.
/// * `Output` true_expected_count: A batch_size * num_true matrix, representing
/// the number of times each candidate is expected to occur in a batch
/// of sampled candidates. If unique=true, then this is a probability.
/// * `Output` sampled_expected_count: A vector of length num_sampled, for each sampled
/// candidate representing the number of times the candidate is expected
/// to occur in a batch of sampled candidates.  If unique=true, then this is a
/// probability.
class UniformCandidateSampler {
 public:
  /// Optional attribute setters for UniformCandidateSampler
  struct Attrs {
    /// If either seed or seed2 are set to be non-zero, the random number
    /// generator is seeded by the given seed.  Otherwise, it is seeded by a
    /// random seed.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Seed(int64 x) {
      Attrs ret = *this;
      ret.seed_ = x;
      return ret;
    }

/// An second seed to avoid seed collision.
    ///
    /// Defaults to 0
    TF_MUST_USE_RESULT Attrs Seed2(int64 x) {
      Attrs ret = *this;
      ret.seed2_ = x;
      return ret;
    }

int64 seed_ = 0;
    int64 seed2_ = 0;
  };
  UniformCandidateSampler(const ::tensorflow::Scope& scope, ::tensorflow::Input
                        true_classes, int64 num_true, int64 num_sampled, bool
                        unique, int64 range_max);
  UniformCandidateSampler(const ::tensorflow::Scope& scope, ::tensorflow::Input
                        true_classes, int64 num_true, int64 num_sampled, bool
                        unique, int64 range_max, const
                        UniformCandidateSampler::Attrs& attrs);

static Attrs Seed(int64 x) {
    return Attrs().Seed(x);
  }
  static Attrs Seed2(int64 x) {
    return Attrs().Seed2(x);
  }

Operation operation;
  ::tensorflow::Output sampled_candidates;
  ::tensorflow::Output true_expected_count;
  ::tensorflow::Output sampled_expected_count;
};

/// @}

}  // namespace ops
}  // namespace tensorflow

#endif  // TENSORFLOW_CC_OPS_CANDIDATE_SAMPLING_OPS_H_