parameters: (self,
                 mu,
                 chol,
                 validate_args=True,
                 allow_nan_stats=False,
                 name="MultivariateNormalCholesky")
docstring: Multivariate Normal distributions on `R^k`.
  
      User must provide means `mu` and `chol` which holds the (batch) Cholesky
      factors, such that the covariance of each batch member is `chol chol^T`.
  
      Args:
        mu: `(N+1)-D` floating point tensor with shape `[N1,...,Nb, k]`,
          `b >= 0`.
        chol: `(N+2)-D` `Tensor` with same `dtype` as `mu` and shape
          `[N1,...,Nb, k, k]`.  The upper triangular part is ignored (treated as
          though it is zero), and the diagonal must be positive.
        validate_args: Whether to validate input with asserts.  If `validate_args`
          is `False`, and the inputs are invalid, correct behavior is not
          guaranteed.
        allow_nan_stats:  `Boolean`, default `False`.  If `False`, raise an
          exception if a statistic (e.g. mean/mode/etc...) is undefined for any
          batch member If `True`, batch members with valid parameters leading to
          undefined statistics will return NaN for this statistic.
        name: The name to give Ops created by the initializer.
  
      Raises:
        TypeError: If `mu` and `chol` are different dtypes.
docstring_summary: Multivariate Normal distributions on `R^k`.
docstring_tokens: Multivariate Normal distributions on R^k .
function: def __init__(self,
                 mu,
                 chol,
                 validate_args=True,
                 allow_nan_stats=False,
                 name="MultivariateNormalCholesky"):
      """Multivariate Normal distributions on `R^k`.
  
      User must provide means `mu` and `chol` which holds the (batch) Cholesky
      factors, such that the covariance of each batch member is `chol chol^T`.
  
      Args:
        mu: `(N+1)-D` floating point tensor with shape `[N1,...,Nb, k]`,
          `b >= 0`.
        chol: `(N+2)-D` `Tensor` with same `dtype` as `mu` and shape
          `[N1,...,Nb, k, k]`.  The upper triangular part is ignored (treated as
          though it is zero), and the diagonal must be positive.
        validate_args: Whether to validate input with asserts.  If `validate_args`
          is `False`, and the inputs are invalid, correct behavior is not
          guaranteed.
        allow_nan_stats:  `Boolean`, default `False`.  If `False`, raise an
          exception if a statistic (e.g. mean/mode/etc...) is undefined for any
          batch member If `True`, batch members with valid parameters leading to
          undefined statistics will return NaN for this statistic.
        name: The name to give Ops created by the initializer.
  
      Raises:
        TypeError: If `mu` and `chol` are different dtypes.
      """
      cov = operator_pd_cholesky.OperatorPDCholesky(chol, verify_pd=validate_args)
      super(MultivariateNormalCholesky, self).__init__(
          mu,
          cov,
          allow_nan_stats=allow_nan_stats,
          validate_args=validate_args,
          name=name)
function_tokens: def __init__ ( self , mu , chol , validate_args = True , allow_nan_stats = False , name = "MultivariateNormalCholesky" ) : cov = operator_pd_cholesky . OperatorPDCholesky ( chol , verify_pd = validate_args ) super ( MultivariateNormalCholesky , self ) . __init__ ( mu , cov , allow_nan_stats = allow_nan_stats , validate_args = validate_args , name = name )