code: def logsumexp_ne(x, axis=0):
      xmax = np.array(x).max(axis=axis)
      # xmax_e = np.expand_dims(xmax, axis)
      x = ne.evaluate("sum(exp(x - xmax_e), axis=%d)" % axis)
      x = ne.evaluate("xmax + log(x)")
      infs = np.isinf(xmax)
      if np.ndim(x) > 0:
          x[infs] = xmax[infs]
      elif infs:
          x = xmax
      return x
  
file_path: espnet/egs/libri_css/asr1/diarization/VB_diarization.py
imports: import numexpr
  import numpy
  import scipy.linalg
  from scipy.sparse import coo_matrix
label_desc: Use image node_id 6 for calling a global function with example usage: logsumexp_ne(x, axis) and returns: x