def _logsum_expbig_minus_expsmall(big, small):
  """Stable evaluation of `Log[exp{big} - exp{small}]`.

  To work correctly, we should have the pointwise relation:  `small <= big`.

  Args:
    big: Floating-point `Tensor`
    small: Floating-point `Tensor` with same `dtype` as `big` and broadcastable
      shape.

  Returns:
    `Tensor` of same `dtype` of `big` and broadcast shape.
  """
  with tf.name_scope("logsum_expbig_minus_expsmall"):
    return tf.math.log1p(-tf.exp(small - big)) + big
def _logsum_expbig_minus_expsmall(big, small):
  """Stable evaluation of `Log[exp{big} - exp{small}]`.

  To work correctly, we should have the pointwise relation:  `small <= big`.

  Args:
    big: Floating-point `Tensor`
    small: Floating-point `Tensor` with same `dtype` as `big` and broadcastable
      shape.

  Returns:
    `Tensor` of same `dtype` of `big` and broadcast shape.
  """
  with tf.name_scope("logsum_expbig_minus_expsmall"):
    return tf.math.log1p(-tf.exp(small - big)) + big
def _logsum_expbig_minus_expsmall ( big , small ) : with tf . name_scope ( "logsum_expbig_minus_expsmall" ) : return tf . math . log1p ( - tf . exp ( small - big ) ) + big
Stable evaluation of `Log[exp{big} - exp{small}]`.

  To work correctly, we should have the pointwise relation:  `small <= big`.

  Args:
    big: Floating-point `Tensor`
    small: Floating-point `Tensor` with same `dtype` as `big` and broadcastable
      shape.

  Returns:
    `Tensor` of same `dtype` of `big` and broadcast shape.
Stable evaluation of Log [ exp { big } - exp { small } ] .