parameters: (self, x, name="log_prob")
docstring: Log prob of observations in `x` under these Normal distribution(s).
  
      Args:
        x: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
        name: The name to give this op.
  
      Returns:
        log_prob: tensor of dtype `dtype`, the log-PDFs of `x`.
docstring_summary: Log prob of observations in `x` under these Normal distribution(s).
docstring_tokens: Log prob of observations in x under these Normal distribution ( s ) .
function: def log_prob(self, x, name="log_prob"):
      """Log prob of observations in `x` under these Normal distribution(s).
  
      Args:
        x: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
        name: The name to give this op.
  
      Returns:
        log_prob: tensor of dtype `dtype`, the log-PDFs of `x`.
      """
      with ops.name_scope(self.name):
        with ops.op_scope([self._mu, self._sigma, x], name):
          x = ops.convert_to_tensor(x)
          if x.dtype != self.dtype:
            raise TypeError("Input x dtype does not match dtype: %s vs. %s"
                            % (x.dtype, self.dtype))
          log_2_pi = constant_op.constant(math.log(2 * math.pi), dtype=self.dtype)
          return (-0.5*log_2_pi - math_ops.log(self._sigma)
                  -0.5*math_ops.square((x - self._mu) / self._sigma))
function_tokens: def log_prob ( self , x , name = "log_prob" ) : with ops . name_scope ( self . name ) : with ops . op_scope ( [ self . _mu , self . _sigma , x ] , name ) : x = ops . convert_to_tensor ( x ) if x . dtype != self . dtype : raise TypeError ( "Input x dtype does not match dtype: %s vs. %s" % ( x . dtype , self . dtype ) ) log_2_pi = constant_op . constant ( math . log ( 2 * math . pi ) , dtype = self . dtype ) return ( - 0.5 * log_2_pi - math_ops . log ( self . _sigma ) - 0.5 * math_ops . square ( ( x - self . _mu ) / self . _sigma ) )