parameters: (self, x, name="log_prob")
docstring: Log prob of observations in `x` under these Gamma distribution(s).
  
      Args:
        x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
        name: The name to give this op.
  
      Returns:
        log_prob: tensor of dtype `dtype`, the log-PDFs of `x`.
  
      Raises:
        TypeError: if `x` and `alpha` are different dtypes.
docstring_summary: Log prob of observations in `x` under these Gamma distribution(s).
docstring_tokens: Log prob of observations in x under these Gamma distribution ( s ) .
function: def log_prob(self, x, name="log_prob"):
      """Log prob of observations in `x` under these Gamma distribution(s).
  
      Args:
        x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
        name: The name to give this op.
  
      Returns:
        log_prob: tensor of dtype `dtype`, the log-PDFs of `x`.
  
      Raises:
        TypeError: if `x` and `alpha` are different dtypes.
      """
      with ops.name_scope(self.name):
        with ops.op_scope([self._alpha, self._beta, x], name):
          alpha = self._alpha
          beta = self._beta
          x = ops.convert_to_tensor(x)
          x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
                                                 self.validate_args else [], x)
          contrib_tensor_util.assert_same_float_dtype(tensors=[x,],
                                                      dtype=self.dtype)
  
          return (alpha * math_ops.log(beta) + (alpha - 1) * math_ops.log(x) -
                  beta * x - math_ops.lgamma(self._alpha))
function_tokens: def log_prob ( self , x , name = "log_prob" ) : with ops . name_scope ( self . name ) : with ops . op_scope ( [ self . _alpha , self . _beta , x ] , name ) : alpha = self . _alpha beta = self . _beta x = ops . convert_to_tensor ( x ) x = control_flow_ops . with_dependencies ( [ check_ops . assert_positive ( x ) ] if self . validate_args else [ ] , x ) contrib_tensor_util . assert_same_float_dtype ( tensors = [ x , ] , dtype = self . dtype ) return ( alpha * math_ops . log ( beta ) + ( alpha - 1 ) * math_ops . log ( x ) - beta * x - math_ops . lgamma ( self . _alpha ) )