parameters: (
        self,
        temperature,
        logits=None,
        probs=None,
        dtype=None,
        validate_args=False,
        allow_nan_stats=True,
        name="RelaxedOneHotCategorical")
docstring: Initialize RelaxedOneHotCategorical using class log-probabilities.
  
      Args:
        temperature: An 0-D `Tensor`, representing the temperature
          of a set of RelaxedOneHotCategorical distributions. The temperature
          should be positive.
        logits: An N-D `Tensor`, `N >= 1`, representing the log probabilities
          of a set of RelaxedOneHotCategorical distributions. The first
          `N - 1` dimensions index into a batch of independent distributions and
          the last dimension represents a vector of logits for each class. Only
          one of `logits` or `probs` should be passed in.
        probs: An N-D `Tensor`, `N >= 1`, representing the probabilities
          of a set of RelaxedOneHotCategorical distributions. The first `N - 1`
          dimensions index into a batch of independent distributions and the last
          dimension represents a vector of probabilities for each class. Only one
          of `logits` or `probs` should be passed in.
        dtype: The type of the event samples (default: inferred from
          logits/probs).
        validate_args: Unused in this distribution.
        allow_nan_stats: Python `bool`, default `True`. If `False`, raise an
          exception if a statistic (e.g. mean/mode/etc...) is undefined for any
          batch member. If `True`, batch members with valid parameters leading to
          undefined statistics will return NaN for this statistic.
        name: A name for this distribution (optional).
docstring_summary: Initialize RelaxedOneHotCategorical using class log-probabilities.
docstring_tokens: Initialize RelaxedOneHotCategorical using class log - probabilities .
function: def __init__(
        self,
        temperature,
        logits=None,
        probs=None,
        dtype=None,
        validate_args=False,
        allow_nan_stats=True,
        name="RelaxedOneHotCategorical"):
      """Initialize RelaxedOneHotCategorical using class log-probabilities.
  
      Args:
        temperature: An 0-D `Tensor`, representing the temperature
          of a set of RelaxedOneHotCategorical distributions. The temperature
          should be positive.
        logits: An N-D `Tensor`, `N >= 1`, representing the log probabilities
          of a set of RelaxedOneHotCategorical distributions. The first
          `N - 1` dimensions index into a batch of independent distributions and
          the last dimension represents a vector of logits for each class. Only
          one of `logits` or `probs` should be passed in.
        probs: An N-D `Tensor`, `N >= 1`, representing the probabilities
          of a set of RelaxedOneHotCategorical distributions. The first `N - 1`
          dimensions index into a batch of independent distributions and the last
          dimension represents a vector of probabilities for each class. Only one
          of `logits` or `probs` should be passed in.
        dtype: The type of the event samples (default: inferred from
          logits/probs).
        validate_args: Unused in this distribution.
        allow_nan_stats: Python `bool`, default `True`. If `False`, raise an
          exception if a statistic (e.g. mean/mode/etc...) is undefined for any
          batch member. If `True`, batch members with valid parameters leading to
          undefined statistics will return NaN for this statistic.
        name: A name for this distribution (optional).
      """
      dist = ExpRelaxedOneHotCategorical(temperature,
                                         logits=logits,
                                         probs=probs,
                                         dtype=dtype,
                                         validate_args=validate_args,
                                         allow_nan_stats=allow_nan_stats)
      super(RelaxedOneHotCategorical, self).__init__(dist,
                                                     bijectors.Exp(),
                                                     name=name)
function_tokens: def __init__ ( self , temperature , logits = None , probs = None , dtype = None , validate_args = False , allow_nan_stats = True , name = "RelaxedOneHotCategorical" ) : dist = ExpRelaxedOneHotCategorical ( temperature , logits = logits , probs = probs , dtype = dtype , validate_args = validate_args , allow_nan_stats = allow_nan_stats ) super ( RelaxedOneHotCategorical , self ) . __init__ ( dist , bijectors . Exp ( ) , name = name )