code: def _logsum_expbig_minus_expsmall(big, small):
      """Numerically stable evaluation of `log(exp(big) - exp(small))`.
  
      This assumes `small <= big` and arguments that can be broadcast against each
      other.
  
      Args:
        big: Floating-point `tf.Tensor`.
        small: Floating-point `tf.Tensor`.
  
      Returns:
        `tf.Tensor` containing the result.
      """
      with tf.name_scope("logsum_expbig_minus_expsmall"):
          # Have to special case `inf` and `-inf` since otherwise we get a NaN
          # out of the exp (if both small and big are -inf).
          return tf.where(
              tf.math.is_inf(big),
              big,
              tf.math.log1p(-tf.exp(small - big)) + big,
          )
  
imports: import tensorflow
  import tensorflow_probability
  from tensorflow_compression.python.distributions import helpers
label_desc: Use image node_id 1 for calling a global function with example usage: _logsum_expbig_minus_expsmall(big, small) and returns: tf