text: # Copyright 2018 Google LLC
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     https://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as onp
  import scipy.misc as osp_misc
  
  from .. import lax
  from ..numpy.lax_numpy import _wraps, _reduction_dims, _constant_like
  
  
  @_wraps(osp_misc.logsumexp)
  def logsumexp(a, axis=None, b=None, keepdims=False, return_sign=False):
    if b is not None or return_sign:
      raise NotImplementedError("Only implemented for b=None, return_sign=False")
    dims = _reduction_dims(a, axis)
    shape = lax.subvals(onp.shape(a), zip(dims, (1,) * len(dims)))
    dimadd = lambda x: lax.reshape(x, shape)
    amax = lax.reduce(a, _constant_like(a, -onp.inf), lax.max, dims)
    amax_singletons = dimadd(amax)
    out = lax.add(lax.log(lax.reduce(lax.exp(lax.sub(a, amax_singletons)),
                                     _constant_like(a, 0), lax.add, dims)), amax)
    return dimadd(out) if keepdims else out
  