class_docstr: The log-logistic distribution.
class_docstr_tok: ["The","log-logistic","distribution","."]
docstr: Construct a log-logistic distribution.
  
  The LogLogistic distribution models positive-valued random variables
  whose logarithm is a logistic distribution with location `loc` and
  scale `scale`. It is constructed as the exponential
  transformation of a Logistic distribution.
  
  Args:
    loc: Floating-point `Tensor`; the location of the underlying logistic
      distribution(s).
    scale: Floating-point `Tensor`; the scale of the underlying logistic
      distribution(s).
    validate_args: Python `bool`, default `False`. Whether to validate input
      with asserts. If `validate_args` is `False`, and the inputs are invalid,
      correct behavior is not guaranteed.
    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: The name to give Ops created by the initializer.
docstr_tok: ["Construct","a","log-logistic","distribution",".","The","LogLogistic","distribution","models","positive-valued","random","variables","whose","logarithm","is","a","logistic","distribution","with","location","`","loc","`","and","scale","`","scale","`",".","It","is","constructed","as","the","exponential","transformation","of","a","Logistic","distribution",".","Args",":","loc",":","Floating-point","`","Tensor","`",";","the","location","of","the","underlying","logistic","distribution","(","s",")",".","scale",":","Floating-point","`","Tensor","`",";","the","scale","of","the","underlying","logistic","distribution","(","s",")",".","validate_args",":","Python","`","bool","`",",","default","`","False","`",".","Whether","to","validate","input","with","asserts",".","If","`","validate_args","`","is","`","False","`",",","and","the","inputs","are","invalid",",","correct","behavior","is","not","guaranteed",".","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",":","The","name","to","give","Ops","created","by","the","initializer","."]
code: def __init__(
      self,
      loc,
      scale,
      validate_args=False,
      allow_nan_stats=True,
      name="LogLogistic",
  ):
      """Construct a log-logistic distribution.
  
      The LogLogistic distribution models positive-valued random variables
      whose logarithm is a logistic distribution with location `loc` and
      scale `scale`. It is constructed as the exponential
      transformation of a Logistic distribution.
  
      Args:
        loc: Floating-point `Tensor`; the location of the underlying logistic
          distribution(s).
        scale: Floating-point `Tensor`; the scale of the underlying logistic
          distribution(s).
        validate_args: Python `bool`, default `False`. Whether to validate input
          with asserts. If `validate_args` is `False`, and the inputs are invalid,
          correct behavior is not guaranteed.
        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: The name to give Ops created by the initializer.
      """
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          super(LogLogistic, self).__init__(
              distribution=logistic.Logistic(
                  loc=loc, scale=scale, allow_nan_stats=allow_nan_stats
              ),
              bijector=exp_bijector.Exp(),
              validate_args=validate_args,
              parameters=parameters,
              name=name,
          )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
label_desc: Use image node_id 1 to create a new LogLogistic object from inherited base classes: transformed_distribution with example: obj = LogLogistic(loc, scale, validate_args, allow_nan_stats, name)