class_docstr: The matrix Wishart distribution on positive definite matrices.
  
  This distribution is defined by a scalar number of degrees of freedom `df` and
  an instance of `LinearOperator`, which provides matrix-free access to a
  symmetric positive definite operator, which defines the scale matrix.
  
  #### Mathematical Details
  
  The probability density function (pdf) is,
  
  ```none
  pdf(X; df, scale) = det(X)**(0.5 (df-k-1)) exp(-0.5 tr[inv(scale) X]) / Z
  Z = 2**(0.5 df k) |det(scale)|**(0.5 df) Gamma_k(0.5 df)
  ```
  
  where:
  
  * `df >= k` denotes the degrees of freedom,
  * `scale` is a symmetric, positive definite, `k x k` matrix,
  * `Z` is the normalizing constant, and,
  * `Gamma_k` is the [multivariate Gamma function](
    https://en.wikipedia.org/wiki/Multivariate_gamma_function).
  
  #### Examples
  
  See the `Wishart` class for examples of initializing and using this class.
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code: def _default_event_space_bijector(self):
      # TODO(b/145620027) Finalize choice of bijector.
      tril_bijector = chain_bijector.Chain(
          [
              transform_diagonal_bijector.TransformDiagonal(
                  diag_bijector=softplus_bijector.Softplus(
                      validate_args=self.validate_args
                  ),
                  validate_args=self.validate_args,
              ),
              fill_scale_tril_bijector.FillScaleTriL(
                  validate_args=self.validate_args
              ),
          ],
          validate_args=self.validate_args,
      )
      if self.input_output_cholesky:
          return tril_bijector
      return chain_bijector.Chain(
          [
              cholesky_outer_product_bijector.CholeskyOuterProduct(
                  validate_args=self.validate_args
              ),
              tril_bijector,
          ],
          validate_args=self.validate_args,
      )
  
imports: import math
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import cholesky_outer_product
  from tensorflow_probability.python.bijectors import fill_scale_tril
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.bijectors import transform_diagonal
  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.internal import tensorshape_util
label_desc: Use image node_id 23 for calling the WishartLinearOperator obj's underlying member method code with example usage: obj._default_event_space_bijector() and returns: chain_bijector, tril_bijector