parameters: (self, x, name="prob")
docstring: The PDF of observations `x` under these Multivariate Normals.
  
      `x` is a batch vector with compatible shape if `x` is a `Tensor` whose
      shape can be broadcast up to either:
  
      ````
      self.batch_shape + self.event_shape
      OR
      [M1,...,Mm] + self.batch_shape + self.event_shape
      ```
  
      Args:
        x: Compatible batch vector with same `dtype` as this distribution.
        name: The name to give this op.
  
      Returns:
        prob: tensor of dtype `dtype`, the prob values of `x`.
docstring_summary: The PDF of observations `x` under these Multivariate Normals.
docstring_tokens: The PDF of observations x under these Multivariate Normals .
function: def prob(self, x, name="prob"):
      """The PDF of observations `x` under these Multivariate Normals.
  
      `x` is a batch vector with compatible shape if `x` is a `Tensor` whose
      shape can be broadcast up to either:
  
      ````
      self.batch_shape + self.event_shape
      OR
      [M1,...,Mm] + self.batch_shape + self.event_shape
      ```
  
      Args:
        x: Compatible batch vector with same `dtype` as this distribution.
        name: The name to give this op.
  
      Returns:
        prob: tensor of dtype `dtype`, the prob values of `x`.
      """
      with ops.name_scope(self.name):
        with ops.op_scope([self._mu, x] + self._cov.inputs, name):
          return math_ops.exp(self.log_prob(x))
function_tokens: def prob ( self , x , name = "prob" ) : with ops . name_scope ( self . name ) : with ops . op_scope ( [ self . _mu , x ] + self . _cov . inputs , name ) : return math_ops . exp ( self . log_prob ( x ) )