parameters: (true_classes, num_true, num_sampled, unique,
                                    range_max, seed=None, name=None)
return_statement: return gen_candidate_sampling_ops._log_uniform_candidate_sampler(
        true_classes, num_true, num_sampled, unique, range_max, seed=seed1,
        seed2=seed2, name=name)
docstring: Samples a set of classes using a log-uniform (Zipfian) 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 for this operation is an approximately log-uniform
    or Zipfian distribution:
  
    `P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)`
  
    This sampler is useful when the target classes approximately follow such
    a distribution - for example, if the classes represent words in a lexicon
    sorted in decreasing order of frequency. If your classes are not ordered by
    decreasing frequency, do not use this op.
  
    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.
      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 a log-uniform (Zipfian) base distribution.
docstring_tokens: Samples a set of classes using a log - uniform ( Zipfian ) base distribution .
function: def log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique,
                                    range_max, seed=None, name=None):
    """Samples a set of classes using a log-uniform (Zipfian) 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 for this operation is an approximately log-uniform
    or Zipfian distribution:
  
    `P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)`
  
    This sampler is useful when the target classes approximately follow such
    a distribution - for example, if the classes represent words in a lexicon
    sorted in decreasing order of frequency. If your classes are not ordered by
    decreasing frequency, do not use this op.
  
    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.
      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._log_uniform_candidate_sampler(
        true_classes, num_true, num_sampled, unique, range_max, seed=seed1,
        seed2=seed2, name=name)
function_tokens: def log_uniform_candidate_sampler ( true_classes , num_true , num_sampled , unique , range_max , seed = None , name = None ) : seed1 , seed2 = random_seed . get_seed ( seed ) return gen_candidate_sampling_ops . _log_uniform_candidate_sampler ( true_classes , num_true , num_sampled , unique , range_max , seed = seed1 , seed2 = seed2 , name = name )