code: def load_trained_model(model_path, training=True):
      """Load the trained model for recognition.
  
      Args:
          model_path (str): Path to model.***.best
          training (bool): Training mode specification for transducer model.
  
      Returns:
          model (torch.nn.Module): Trained model.
          train_args (Namespace): Trained model arguments.
  
      """
      idim, odim, train_args = get_model_conf(
          model_path,
          os.path.join(os.path.dirname(model_path), "model.json"),
      )
  
      logging.info(f"Reading model parameters from {model_path}")
  
      if hasattr(train_args, "model_module"):
          model_module = train_args.model_module
      else:
          model_module = "espnet.nets.pytorch_backend.e2e_asr:E2E"
  
      # CTC Loss is not needed, default to builtin to prevent import errors
      if hasattr(train_args, "ctc_type"):
          train_args.ctc_type = "builtin"
  
      model_class = dynamic_import(model_module)
  
      if "transducer" in model_module:
          model = model_class(idim, odim, train_args, training=training)
          custom_torch_load(model_path, model, training=training)
      else:
          model = model_class(idim, odim, train_args)
          torch_load(model_path, model)
  
      return model, train_args
  
file_path: espnet/espnet/asr/pytorch_backend/asr_init.py
imports: import logging
  import os
  import re
  from collections import OrderedDict
  import torch
  from espnet.asr.asr_utils import get_model_conf, torch_load
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.mt_interface import MTInterface
  from espnet.nets.pytorch_backend.transducer.utils import custom_torch_load
  from espnet.nets.tts_interface import TTSInterface
  from espnet.utils.dynamic_import import dynamic_import
label_desc: Use image node_id 7 for calling a global function with example usage: load_trained_model(model_path, training) and returns: model,  train_args