parameters: (self,
                 mu,
                 cov,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="MultivariateNormalCov")
docstring: Multivariate Normal distributions on `R^k`.
  
      User must provide means `mu`, and an instance of `OperatorPDBase`, `cov`,
      which determines the covariance.
  
      Args:
        mu: Floating point tensor with shape `[N1,...,Nb, k]`, `b >= 0`.
        cov: Instance of `OperatorPDBase` with same `dtype` as `mu` and shape
          `[N1,...,Nb, k, k]`.
        validate_args: `Boolean`, default `False`.  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 `True`.  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 `cov` are different dtypes.
docstring_summary: Multivariate Normal distributions on `R^k`.
docstring_tokens: Multivariate Normal distributions on R^k .
function: def __init__(self,
                 mu,
                 cov,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="MultivariateNormalCov"):
      """Multivariate Normal distributions on `R^k`.
  
      User must provide means `mu`, and an instance of `OperatorPDBase`, `cov`,
      which determines the covariance.
  
      Args:
        mu: Floating point tensor with shape `[N1,...,Nb, k]`, `b >= 0`.
        cov: Instance of `OperatorPDBase` with same `dtype` as `mu` and shape
          `[N1,...,Nb, k, k]`.
        validate_args: `Boolean`, default `False`.  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 `True`.  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 `cov` are different dtypes.
      """
      with ops.name_scope(name) as ns:
        with ops.name_scope("init", values=[mu] + cov.inputs):
          self._mu = array_ops.identity(mu, name="mu")
          self._cov = cov
          self._validate_args = validate_args  # Needed by _assert_valid_mu.
          self._mu = self._assert_valid_mu(self._mu)
          super(_MultivariateNormalOperatorPD, self).__init__(
              dtype=self._mu.dtype,
              parameters={"mu": self._mu, "cov": self._cov},
              is_reparameterized=True,
              is_continuous=True,
              validate_args=validate_args,
              allow_nan_stats=allow_nan_stats,
              name=ns)
function_tokens: def __init__ ( self , mu , cov , validate_args = False , allow_nan_stats = True , name = "MultivariateNormalCov" ) : with ops . name_scope ( name ) as ns : with ops . name_scope ( "init" , values = [ mu ] + cov . inputs ) : self . _mu = array_ops . identity ( mu , name = "mu" ) self . _cov = cov self . _validate_args = validate_args # Needed by _assert_valid_mu. self . _mu = self . _assert_valid_mu ( self . _mu ) super ( _MultivariateNormalOperatorPD , self ) . __init__ ( dtype = self . _mu . dtype , parameters = { "mu" : self . _mu , "cov" : self . _cov } , is_reparameterized = True , is_continuous = True , validate_args = validate_args , allow_nan_stats = allow_nan_stats , name = ns )