code: def get_args(tc):
      args = []
      input_names = []
      kwargs_trace = {}
      kwargs_non = {}
      lengths = tc.pop("lengths")
      args.append(torch.LongTensor(lengths))
      input_names.append("lengths")
  
      xs = tc.pop("xs", None)
      if xs is not None:
          args.append(xs)
          input_names.append("xs")
          kwargs_trace["xs"] = xs
          kwargs_non["xs"] = xs
  
      ld = tc.pop("length_dim", None)
      if ld is not None:
          args.append(torch.LongTensor([ld]))
          input_names.append("length_dim")
          kwargs_trace["length_dim"] = torch.LongTensor([ld])
          kwargs_non["length_dim"] = ld
  
      ml = tc.pop("maxlen", None)
      if ml is not None:
          args.append(torch.LongTensor([ml]))
          input_names.append("maxlen")
          kwargs_trace["maxlen"] = torch.LongTensor([ml])
          kwargs_non["maxlen"] = ml
  
      return tuple(args), tuple(input_names), kwargs_trace, kwargs_non
  
file_path: espnet/test/test_nets_utils.py
imports: import pytest
  import torch
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask, trim_by_ctc_posterior
label_desc: Use image node_id 3 for calling a global function with example usage: get_args(tc) and returns: tuple,  tuple,  kwargs_trace,  kwargs_non