code: def fwd_grad_logsumexp(g, ans, x, axis=None, b=1.0, keepdims=False):
      if not keepdims:
          if isinstance(axis, int):
              ans = np.expand_dims(ans, axis)
          elif isinstance(axis, tuple):
              for ax in sorted(axis):
                  ans = np.expand_dims(ans, ax)
      return np.sum(
          g * b * np.exp(x - ans), axis=axis, keepdims=keepdims
      )
  
file_path: autograd/autograd/scipy/special.py
imports: from __future__ import absolute_import
  import scipy.special
  import autograd.numpy
  from autograd.extend import primitive, defvjp, defjvp
  from autograd.numpy.numpy_vjps import unbroadcast_f, repeat_to_match_shape
label_desc: Use image node_id 3 for calling a global function with example usage: fwd_grad_logsumexp(g, ans, x, axis, b, keepdims) and returns: np