parameters: (self,
                 df,
                 scale,
                 cholesky_input_output_matrices=False,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="WishartCholesky")
docstring: Construct Wishart distributions.
  
      Args:
        df: `float` or `double` `Tensor`. Degrees of freedom, must be greater than
          or equal to dimension of the scale matrix.
        scale: `float` or `double` `Tensor`. The Cholesky factorization of
          the symmetric positive definite scale matrix of the distribution.
        cholesky_input_output_matrices: Python `bool`. Any function which whose
          input or output is a matrix assumes the input is Cholesky and returns a
          Cholesky factored matrix. Example `log_prob` input takes a Cholesky and
          `sample_n` returns a Cholesky when
          `cholesky_input_output_matrices=True`.
        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.
        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.
        name: Python `str` name prefixed to Ops created by this class.
docstring_summary: Construct Wishart distributions.
docstring_tokens: Construct Wishart distributions .
function: def __init__(self,
                 df,
                 scale,
                 cholesky_input_output_matrices=False,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="WishartCholesky"):
      """Construct Wishart distributions.
  
      Args:
        df: `float` or `double` `Tensor`. Degrees of freedom, must be greater than
          or equal to dimension of the scale matrix.
        scale: `float` or `double` `Tensor`. The Cholesky factorization of
          the symmetric positive definite scale matrix of the distribution.
        cholesky_input_output_matrices: Python `bool`. Any function which whose
          input or output is a matrix assumes the input is Cholesky and returns a
          Cholesky factored matrix. Example `log_prob` input takes a Cholesky and
          `sample_n` returns a Cholesky when
          `cholesky_input_output_matrices=True`.
        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.
        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.
        name: Python `str` name prefixed to Ops created by this class.
      """
      parameters = locals()
      with ops.name_scope(name, values=[scale]):
        super(WishartCholesky, self).__init__(
            df=df,
            scale_operator_pd=operator_pd_cholesky.OperatorPDCholesky(
                scale, verify_pd=validate_args),
            cholesky_input_output_matrices=cholesky_input_output_matrices,
            validate_args=validate_args,
            allow_nan_stats=allow_nan_stats,
            name=name)
      self._parameters = parameters
function_tokens: def __init__ ( self , df , scale , cholesky_input_output_matrices = False , validate_args = False , allow_nan_stats = True , name = "WishartCholesky" ) : parameters = locals ( ) with ops . name_scope ( name , values = [ scale ] ) : super ( WishartCholesky , self ) . __init__ ( df = df , scale_operator_pd = operator_pd_cholesky . OperatorPDCholesky ( scale , verify_pd = validate_args ) , cholesky_input_output_matrices = cholesky_input_output_matrices , validate_args = validate_args , allow_nan_stats = allow_nan_stats , name = name ) self . _parameters = parameters