code: def log_loosum_exp(logx, axis=None, keepdims=False, name=None):
      """Computes the log-leave-one-out-sum of `exp(logx)`.
  
      Args:
        logx: Floating-type `Tensor` representing `log(x)` where `x` is some
          positive value.
        axis: The dimensions to sum across. If `None` (the default), reduces all
          dimensions. Must be in the range `[-rank(logx), rank(logx)]`.
          Default value: `None` (i.e., reduce over all dims).
        keepdims: If true, retains reduced dimensions with length 1.
          Default value: `False` (i.e., keep all dims in `log_sum_x`).
        name: Python `str` name prefixed to Ops created by this function.
          Default value: `None` (i.e., `"log_loosum_exp"`).
  
      Returns:
        log_loosum_exp: `Tensor` with same shape and dtype as `logx` representing
          the natural-log of the sum of `exp(logx)` except that the element
          `logx[i]` is removed.
        log_sum_x: `logx.dtype` `Tensor` corresponding to the natural-log of the
          sum of `exp(logx)`. Has reduced shape of `logx` (per `axis` and
          `keepdims`).
      """
      with tf.name_scope(name or "log_loosum_exp"):
          return _log_loosum_exp_impl(
              logx, axis, keepdims, compute_mean=False
          )[:2]
  
file_path: probability/tensorflow_probability/python/stats/leave_one_out.py
imports: import numpy
  import tensorflow.compat.v2
  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.math.generic import log_add_exp
  from tensorflow_probability.python.math.generic import softplus_inverse
label_desc: Use image node_id 1 for calling a global function with example usage: log_loosum_exp(logx, axis, keepdims, name) and returns: _log_loosum_exp_impl