code: 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:
        log_sub_exp: `Tensor` of same `dtype` of `big` and broadcast shape.
      """
      with tf.name_scope("logsum_expbig_minus_expsmall"):
          return big + tf.math.log1p(-tf.exp(small - big))
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import auto_composite_tensor
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 1 for calling a global function with example usage: _logsum_expbig_minus_expsmall(big, small) and returns: unknown