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: Scale parameter and also the lower bound of the support.
docstr_tok: ["Scale","parameter","and","also","the","lower","bound","of","the","support","."]
code: def scale(self):
      """Scale parameter and also the lower bound of the support."""
      return self._scale
  
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 3 for calling the Pareto obj's underlying member method code with example usage: obj.scale() and returns: self