parameters: (true_classes,
                                      num_true,
                                      num_sampled,
                                      unique,
                                      range_max,
                                      vocab_file='',
                                      distortion=1.0,
                                      num_reserved_ids=0,
                                      num_shards=1,
                                      shard=0,
                                      unigrams=(),
                                      seed=None,
                                      name=None)
return_statement: return gen_candidate_sampling_ops._fixed_unigram_candidate_sampler(
        true_classes, num_true, num_sampled, unique, range_max,
        vocab_file=vocab_file, distortion=distortion,
        num_reserved_ids=num_reserved_ids, num_shards=num_shards, shard=shard,
        unigrams=unigrams, seed=seed1, seed2=seed2, name=name)
docstring: Samples a set of classes using the provided (fixed) base distribution.
  
    This operation randomly samples a tensor of sampled classes
    (`sampled_candidates`) from the range of integers `[0, range_max)`.
  
    The elements of `sampled_candidates` are drawn without replacement
    (if `unique=True`) or with replacement (if `unique=False`) from
    the base distribution.
  
    The base distribution is read from a file or passed in as an
    in-memory array. There is also an option to skew the distribution by
    applying a distortion power to the weights.
  
    In addition, this operation returns tensors `true_expected_count`
    and `sampled_expected_count` representing the number of times each
    of the target classes (`true_classes`) and the sampled
    classes (`sampled_candidates`) is expected to occur in an average
    tensor of sampled classes.  These values correspond to `Q(y|x)`
    defined in [this
    document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
    If `unique=True`, then these are post-rejection probabilities and we
    compute them approximately.
  
    Args:
      true_classes: A `Tensor` of type `int64` and shape `[batch_size,
        num_true]`. The target classes.
      num_true: An `int`.  The number of target classes per training example.
      num_sampled: An `int`.  The number of classes to randomly sample.
      unique: A `bool`. Determines whether all sampled classes in a batch are
        unique.
      range_max: An `int`. The number of possible classes.
      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 operation.
      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 the operation, 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 operation.
      seed: An `int`. An operation-specific seed. Default is 0.
      name: A name for the operation (optional).
  
    Returns:
      sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`.
        The sampled classes.
      true_expected_count: A tensor of type `float`.  Same shape as
        `true_classes`. The expected counts under the sampling distribution
        of each of `true_classes`.
      sampled_expected_count: A tensor of type `float`. Same shape as
        `sampled_candidates`. The expected counts under the sampling distribution
        of each of `sampled_candidates`.
docstring_summary: Samples a set of classes using the provided (fixed) base distribution.
docstring_tokens: Samples a set of classes using the provided ( fixed ) base distribution .
function: def fixed_unigram_candidate_sampler(true_classes,
                                      num_true,
                                      num_sampled,
                                      unique,
                                      range_max,
                                      vocab_file='',
                                      distortion=1.0,
                                      num_reserved_ids=0,
                                      num_shards=1,
                                      shard=0,
                                      unigrams=(),
                                      seed=None,
                                      name=None):
    """Samples a set of classes using the provided (fixed) base distribution.
  
    This operation randomly samples a tensor of sampled classes
    (`sampled_candidates`) from the range of integers `[0, range_max)`.
  
    The elements of `sampled_candidates` are drawn without replacement
    (if `unique=True`) or with replacement (if `unique=False`) from
    the base distribution.
  
    The base distribution is read from a file or passed in as an
    in-memory array. There is also an option to skew the distribution by
    applying a distortion power to the weights.
  
    In addition, this operation returns tensors `true_expected_count`
    and `sampled_expected_count` representing the number of times each
    of the target classes (`true_classes`) and the sampled
    classes (`sampled_candidates`) is expected to occur in an average
    tensor of sampled classes.  These values correspond to `Q(y|x)`
    defined in [this
    document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
    If `unique=True`, then these are post-rejection probabilities and we
    compute them approximately.
  
    Args:
      true_classes: A `Tensor` of type `int64` and shape `[batch_size,
        num_true]`. The target classes.
      num_true: An `int`.  The number of target classes per training example.
      num_sampled: An `int`.  The number of classes to randomly sample.
      unique: A `bool`. Determines whether all sampled classes in a batch are
        unique.
      range_max: An `int`. The number of possible classes.
      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 operation.
      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 the operation, 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 operation.
      seed: An `int`. An operation-specific seed. Default is 0.
      name: A name for the operation (optional).
  
    Returns:
      sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`.
        The sampled classes.
      true_expected_count: A tensor of type `float`.  Same shape as
        `true_classes`. The expected counts under the sampling distribution
        of each of `true_classes`.
      sampled_expected_count: A tensor of type `float`. Same shape as
        `sampled_candidates`. The expected counts under the sampling distribution
        of each of `sampled_candidates`.
  
    """
    seed1, seed2 = random_seed.get_seed(seed)
    return gen_candidate_sampling_ops._fixed_unigram_candidate_sampler(
        true_classes, num_true, num_sampled, unique, range_max,
        vocab_file=vocab_file, distortion=distortion,
        num_reserved_ids=num_reserved_ids, num_shards=num_shards, shard=shard,
        unigrams=unigrams, seed=seed1, seed2=seed2, name=name)
function_tokens: def fixed_unigram_candidate_sampler ( true_classes , num_true , num_sampled , unique , range_max , vocab_file = '' , distortion = 1.0 , num_reserved_ids = 0 , num_shards = 1 , shard = 0 , unigrams = ( ) , seed = None , name = None ) : seed1 , seed2 = random_seed . get_seed ( seed ) return gen_candidate_sampling_ops . _fixed_unigram_candidate_sampler ( true_classes , num_true , num_sampled , unique , range_max , vocab_file = vocab_file , distortion = distortion , num_reserved_ids = num_reserved_ids , num_shards = num_shards , shard = shard , unigrams = unigrams , seed = seed1 , seed2 = seed2 , name = name )