code: def log_add_exp(x, y, name=None):
      """Computes `log(exp(x) + exp(y))` in a numerically stable way.
  
      Args:
        x: `float` `Tensor` broadcastable with `y`.
        y: `float` `Tensor` broadcastable with `x`.
        name: Python `str` name prefixed to Ops created by this function.
          Default value: `None` (i.e., `'log_add_exp'`).
  
      Returns:
        log_add_exp: `log(exp(x) + exp(y))` computed in a numerically stable way.
      """
      with tf.name_scope(name or "log_add_exp"):
          dtype = dtype_util.common_dtype([x, y], dtype_hint=tf.float32)
          x = tf.convert_to_tensor(x, dtype=dtype, name="x")
          y = tf.convert_to_tensor(y, dtype=dtype, name="y")
  
          # The following is similar to using the standard method
          # `tf.maximum(x, y) + tf.math.softplus(-abs(x - y))`
          # to compute `log_add_exp`. However, both `tf.maximum` and
          # `abs(x - y)` have discontinuities in their derivatives
          # along `x == y`.
          # This version ensures that the contribution of the discontinuities
          # to the derivative all cancel leaving a continuous result without
          # changing the domain in which the original was valid.
          larger = tf.maximum(x, y)
          return larger + tf.math.softplus((x - larger) + (y - larger))
  
imports: import collections
  import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import custom_gradient
  from tensorflow_probability.python.internal import distribute_lib
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import variadic_reduce
  from tensorflow_probability.python.math.scan_associative import scan_associative
  from tensorflow.python.util import deprecation
label_desc: Use image node_id 12 for calling a global function with example usage: log_add_exp(x, y, name) and returns: unknown