class_docstr: Pareto distribution.
  
  The Pareto distribution is parameterized by a `scale` and a
  `concentration` parameter.
  
  #### Mathematical Details
  
  The probability density function (pdf) is,
  
  ```none
  pdf(x; alpha, scale, x >= scale) = alpha * scale ** alpha / x ** (alpha + 1)
  ```
  
  where `concentration = alpha`.
  
  Note that `scale` acts as a scaling parameter, since
  `Pareto(c, scale).pdf(x) == Pareto(c, 1.).pdf(x / scale)`.
  
  The support of the distribution is defined on `[scale, infinity)`.
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docstr: Construct Pareto distribution with `concentration` and `scale`.
  
  Args:
    concentration: Floating point tensor. Must contain only positive values.
    scale: Floating point tensor, equivalent to `mode`. `scale` also
      restricts the domain of this distribution to be in `[scale, inf)`.
      Must contain only positive values. Default value: `1`.
    validate_args: Python `bool`, default `False`. When `True` distribution
      parameters are checked for validity despite possibly degrading runtime
      performance. When `False` invalid inputs may silently render incorrect
      outputs. Default value: `False` (i.e. do not validate args).
    allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
      (e.g., mean, mode, variance) use the value '`NaN`' to indicate the
      result is undefined. When `False`, an exception is raised if one or
      more of the statistic's batch members are undefined.
      Default value: `True`.
    name: Python `str` name prefixed to Ops created by this class.
      Default value: 'Pareto'.
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code: def __init__(
      self,
      concentration,
      scale=1.0,
      validate_args=False,
      allow_nan_stats=True,
      name="Pareto",
  ):
      """Construct Pareto distribution with `concentration` and `scale`.
  
      Args:
        concentration: Floating point tensor. Must contain only positive values.
        scale: Floating point tensor, equivalent to `mode`. `scale` also
          restricts the domain of this distribution to be in `[scale, inf)`.
          Must contain only positive values. Default value: `1`.
        validate_args: Python `bool`, default `False`. When `True` distribution
          parameters are checked for validity despite possibly degrading runtime
          performance. When `False` invalid inputs may silently render incorrect
          outputs. Default value: `False` (i.e. do not validate args).
        allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
          (e.g., mean, mode, variance) use the value '`NaN`' to indicate the
          result is undefined. When `False`, an exception is raised if one or
          more of the statistic's batch members are undefined.
          Default value: `True`.
        name: Python `str` name prefixed to Ops created by this class.
          Default value: 'Pareto'.
      """
      parameters = dict(locals())
      with tf.name_scope(name):
          dtype = dtype_util.common_dtype(
              [concentration, scale], dtype_hint=tf.float32
          )
          self._concentration = tensor_util.convert_nonref_to_tensor(
              concentration, name="concentration", dtype=dtype
          )
          self._scale = tensor_util.convert_nonref_to_tensor(
              scale, name="scale", dtype=dtype
          )
          super(Pareto, self).__init__(
              dtype=self._concentration.dtype,
              reparameterization_type=reparameterization.FULLY_REPARAMETERIZED,
              validate_args=validate_args,
              allow_nan_stats=allow_nan_stats,
              parameters=parameters,
              name=name,
          )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  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 Pareto object from inherited base classes: distribution with example: obj = Pareto(concentration, scale, validate_args, allow_nan_stats, name)