code: def _log_sum_exp(x):
      r"""Numerically stable log_sum_exp implementation that prevents overflow"""
  
      axis = len(x.size()) - 1
      m, _ = torch.max(x, dim=axis)
      m2, _ = torch.max(x, dim=axis, keepdim=True)
      return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis))
  
file_path: audio/examples/pipeline_wavernn/losses.py
imports: import math
  import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 1 for calling a global function with example usage: _log_sum_exp(x) and returns: unknown