code: def test_pytorch_transducer_trainable_and_decodable(
      train_dic, recog_dic
  ):
      idim, odim, ilens, olens = get_default_scope_inputs()
  
      train_args = get_default_train_args(**train_dic)
      recog_args = get_default_recog_args(**recog_dic)
  
      model = E2E(idim, odim, train_args)
  
      batch = prepare_inputs(idim, odim, ilens, olens)
  
      # to avoid huge training time, cer/wer report
      # is only enabled at validation steps
      if train_args.report_cer or train_args.report_wer:
          model.training = False
  
      loss = model(*batch)
      loss.backward()
  
      beam_search = BeamSearchTransducer(
          decoder=model.dec,
          joint_network=model.transducer_tasks.joint_network,
          beam_size=recog_args.beam_size,
          lm=recog_args.rnnlm,
          lm_weight=recog_args.lm_weight,
          search_type=recog_args.search_type,
          max_sym_exp=recog_args.max_sym_exp,
          u_max=recog_args.u_max,
          nstep=recog_args.nstep,
          prefix_alpha=recog_args.prefix_alpha,
          score_norm=recog_args.score_norm_transducer,
      )
  
      with torch.no_grad():
          in_data = np.random.randn(20, idim)
  
          model.recognize(in_data, beam_search)
  
imports: import argparse
  import json
  import tempfile
  import numpy
  import pytest
  import torch
  from packaging.version import parse
  import espnet.lm.pytorch_backend.extlm
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.pytorch_backend.asr_init import load_trained_model
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.e2e_asr_transducer import E2E
  from espnet.nets.pytorch_backend.nets_utils import pad_list
label_desc: Use image node_id 7 for calling a global function with example usage: test_pytorch_transducer_trainable_and_decodable(train_dic, recog_dic) without return types