class_docstr: The Generalized Normal distribution.
  
  The Generalized Normal (or Generalized Gaussian) generalizes the Normal
  distribution with an additional shape parameter. It is parameterized by
  location `loc`, scale `scale` and shape `power`.
  
  #### Mathematical details
  
  The probability density function (pdf) is,
  
  ```none
  pdf(x; loc, scale, power) = 1 / (2 * scale * Gamma(1 + 1 / power)) *
                             exp(-(|x - loc| / scale) ^ power)
  ```
  where `loc` is the mean, `scale` is the scale, and,  `power` is the shape
  parameter. If the power is above two, the distribution becomes platykurtic.
  A power equal to two results in a Normal distribution. A power smaller than
  two produces a leptokurtic (heavy-tailed) distribution. Mean and scale behave
  the same way as in the equivalent Normal distribution.
  
  See
  https://en.wikipedia.org/w/index.php?title=Generalized_normal_distribution&oldid=954254464
  for the definitions used here, including CDF, variance and entropy. See
  https://sccn.ucsd.edu/wiki/Generalized_Gaussian_Probability_Density_Function
  for the sampling method used here.
  
  #### Examples
  
  ```python
  import tensorflow_probability as tfp
  tfd = tfp.distributions
  
  dist = tfd.GeneralizedNormal(loc=3.0, scale=2.0, power=1.0)
  dist2 = tfd.GeneralizedNormal(loc=0, scale=[3.0, 4.0], power=[[2.0], [3.0]])
  ```
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docstr: Distribution parameter for shape.
docstr_tok: ["Distribution","parameter","for","shape","."]
code: def power(self):
      """Distribution parameter for shape."""
      return self._power
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python import random
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  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 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.math import special
label_desc: Use image node_id 5 for calling the GeneralizedNormal obj's underlying member method code with example usage: obj.power() and returns: self