code: def taylor_softmax_v1(x, dim=1, n=4, use_log=False):
      assert n % 2 == 0 and n > 0
      fn = torch.ones_like(x)
      denor = 1.0
      for i in range(1, n + 1):
          denor *= i
          fn = fn + x.pow(i) / denor
      out = fn / fn.sum(dim=dim, keepdims=True)
      if use_log:
          out = out.log()
      return out
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
label_desc: Use image node_id 1 for calling a global function with example usage: taylor_softmax_v1(x, dim, n, use_log) and returns: out