code: def test_pytorch_trainable_and_transferable(
      main_model_type, pt_model_type, finetune_dic
  ):
      idim, odim, ilens, olens = get_default_scope_inputs()
      batch = pytorch_prepare_inputs(idim, odim, ilens, olens)
  
      if pt_model_type == "lm":
          pt_args = (
              get_rnnt_args()
              if main_model_type == "rnnt"
              else get_rnn_args()
          )
          pt_model = get_lm(
              pt_args.dlayers, pt_args.dunits, pt_args.char_list
          )
          prefix_tmppath = "_rnnlm"
      else:
          if pt_model_type == "rnn":
              from espnet.nets.pytorch_backend.e2e_asr import E2E
  
              pt_args = get_rnn_args()
          else:
              from espnet.nets.pytorch_backend.e2e_asr_transducer import (
                  E2E,
              )
  
              pt_args = get_rnnt_args()
  
          pt_model = E2E(idim, odim, pt_args)
          prefix_tmppath = ""
  
          loss = pt_model(*batch)
          loss.backward()
  
      if not os.path.exists(".pytest_cache"):
          os.makedirs(".pytest_cache")
  
      tmppath = tempfile.mktemp() + prefix_tmppath
      torch_save(tmppath, pt_model)
  
      # create dummy model.json for saved model to go through
      # get_model_conf(...) called in load_trained_modules method.
      model_conf = os.path.dirname(tmppath) + "/model.json"
      with open(model_conf, "wb") as f:
          f.write(
              json.dumps(
                  (idim, odim, vars(pt_args)),
                  indent=4,
                  ensure_ascii=False,
                  sort_keys=True,
              ).encode("utf_8")
          )
  
      if finetune_dic["enc_init"] is not None:
          finetune_dic["enc_init"] = tmppath
      if finetune_dic["dec_init"] is not None:
          finetune_dic["dec_init"] = tmppath
  
      if main_model_type == "rnn":
          main_args = get_rnn_args(**finetune_dic)
      else:
          main_args = get_rnnt_args(**finetune_dic)
      main_model = load_trained_modules(idim, odim, main_args)
  
      loss = main_model(*batch)
      loss.backward()
  
      if main_model_type == "rnnt":
          beam_search = BeamSearchTransducer(
              decoder=main_model.dec,
              joint_network=main_model.transducer_tasks.joint_network,
              beam_size=1,
              lm=None,
              lm_weight=0.0,
              search_type="default",
              max_sym_exp=2,
              u_max=10,
              nstep=1,
              prefix_alpha=1,
              score_norm=False,
          )
  
          with torch.no_grad():
              in_data = np.random.randn(10, idim)
              main_model.recognize(in_data, beam_search)
      else:
          with torch.no_grad():
              in_data = np.random.randn(10, idim)
              main_model.recognize(
                  in_data, main_args, main_args.char_list
              )
  
file_path: espnet/test/test_asr_init.py
imports: import argparse
  import json
  import os
  import tempfile
  import numpy
  import pytest
  import torch
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.asr_utils import torch_save
  from espnet.asr.pytorch_backend.asr_init import freeze_modules, load_trained_modules
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.nets_utils import pad_list
label_desc: Use image node_id 6 for calling a global function with example usage: test_pytorch_trainable_and_transferable(main_model_type, pt_model_type, finetune_dic) without return types