class_docstr: The log-normal distribution.
class_docstr_tok: ["The","log-normal","distribution","."]
docstr: Construct a log-normal distribution.
  
  The LogNormal distribution models positive-valued random variables
  whose logarithm is normally distributed with mean `loc` and
  standard deviation `scale`. It is constructed as the exponential
  transformation of a Normal distribution.
  
  Args:
    loc: Floating-point `Tensor`; the means of the underlying
      Normal distribution(s).
    scale: Floating-point `Tensor`; the stddevs of the underlying
      Normal 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.
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code: def __init__(
      self,
      loc,
      scale,
      validate_args=False,
      allow_nan_stats=True,
      name="LogNormal",
  ):
      """Construct a log-normal distribution.
  
      The LogNormal distribution models positive-valued random variables
      whose logarithm is normally distributed with mean `loc` and
      standard deviation `scale`. It is constructed as the exponential
      transformation of a Normal distribution.
  
      Args:
        loc: Floating-point `Tensor`; the means of the underlying
          Normal distribution(s).
        scale: Floating-point `Tensor`; the stddevs of the underlying
          Normal 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(LogNormal, self).__init__(
              distribution=normal.Normal(loc=loc, scale=scale),
              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 kullback_leibler
  from tensorflow_probability.python.distributions import normal
  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
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.util.deferred_tensor import DeferredTensor
label_desc: Use image node_id 1 to create a new LogNormal object from inherited base classes: transformed_distribution with example: obj = LogNormal(loc, scale, validate_args, allow_nan_stats, name)