code: def setup_training_model(args):
      """Parse args, load dataset, and build model with criterion."""
      if not torch.cuda.is_available():
          print("Warning: training without CUDA is likely to be slow!")
      else:
          torch.cuda.set_device(args.device_id)
      torch.manual_seed(args.seed)
  
      # Setup task and load dataset
      task = tasks.setup_task(args)
  
      # Build model and criterion
      model = task.build_model(args)
      print("| building criterion")
      criterion = task.build_criterion(args)
      print(
          f"| model {args.arch}, criterion {criterion.__class__.__name__}"
      )
      print(
          f"| num. model params: \
          {sum(p.numel() for p in model.parameters())}"
      )
  
      if args.task == constants.SEMI_SUPERVISED_TASK:
          # TODO(T35638969): hide this inside the task itself, just use self.args
          task.load_dataset(
              split=args.train_subset,
              src_bin_path=args.train_source_binary_path,
              tgt_bin_path=args.train_target_binary_path,
              forward_model=task.forward_model,
              backward_model=task.backward_model,
          )
      elif args.task == "pytorch_translate_denoising_autoencoder":
          task.load_dataset(
              split=args.train_subset,
              src_bin_path=args.train_source_binary_path,
              tgt_bin_path=args.train_target_binary_path,
              seed=args.seed,
              use_noiser=True,
          )
      elif args.task == "dual_learning_task":
          task.load_dataset(split=args.train_subset, seed=args.seed)
      elif args.task == "pytorch_translate_knowledge_distillation":
          task.load_dataset(
              split=args.train_subset,
              src_bin_path=args.train_source_binary_path,
              tgt_bin_path=args.train_target_binary_path,
              weights_file=getattr(args, "train_weights_path", None),
              is_train=True,
          )
      elif args.task == "pytorch_translate_cross_lingual_lm":
          task.load_dataset(args.train_subset, combine=True, epoch=0)
      elif args.task == "pytorch_translate":
          # Support both single and multi path loading for now
          task.load_dataset(
              split=args.train_subset,
              src_bin_path=args.train_source_binary_path,
              tgt_bin_path=args.train_target_binary_path,
              weights_file=getattr(args, "train_weights_path", None),
              is_npz=not args.fairseq_data_format,
          )
      else:
          # Support both single and multi path loading for now
          task.load_dataset(
              split=args.train_subset,
              src_bin_path=args.train_source_binary_path,
              tgt_bin_path=args.train_target_binary_path,
              weights_file=getattr(args, "train_weights_path", None),
          )
  
      if args.task == "dual_learning_task":
          task.load_dataset(split=args.valid_subset, seed=args.seed)
      elif args.task == "pytorch_translate_cross_lingual_lm":
          task.load_dataset(args.valid_subset, combine=True, epoch=0)
      elif args.task == "pytorch_translate":
          task.load_dataset(
              split=args.valid_subset,
              src_bin_path=args.eval_source_binary_path,
              tgt_bin_path=args.eval_target_binary_path,
              is_npz=not args.fairseq_data_format,
          )
      else:
          task.load_dataset(
              split=args.valid_subset,
              src_bin_path=args.eval_source_binary_path,
              tgt_bin_path=args.eval_target_binary_path,
          )
  
      return task, model, criterion
  
imports: import builtins
  import collections
  import datetime
  import faulthandler
  import math
  import multiprocessing.queues
  import os
  import queue
  import random
  import time
  from typing import Any, Callable, Dict, Optional, Tuple
  import numpy
  import torch
  from fairseq import data, distributed_utils, options, progress_bar, tasks, utils
  from fairseq.meters import AverageMeter, StopwatchMeter
  from fairseq.trainer import Trainer
  from pytorch_translate import char_aware_hybrid, char_source_hybrid, checkpoint, constants, evals, hybrid_transformer_rnn, multi_model, options, preprocess, sequence_criterions, transformer, transformer_aan, weighted_criterions
  from pytorch_translate.data import dictionary
  from pytorch_translate.dual_learning import dual_learning_criterion, dual_learning_task
  from pytorch_translate.file_io import PathManager
  from pytorch_translate.research.deliberation_networks import deliberation_networks
  from pytorch_translate.research.knowledge_distillation import dual_decoder_kd_loss, dual_decoder_kd_model, hybrid_dual_decoder_kd_model, knowledge_distillation_loss
  from pytorch_translate.research.rescore import cloze_transformer_model, rescoring_criterion
  from pytorch_translate.word_prediction import word_prediction_criterion, word_prediction_model
  from pytorch_translate import char_source_model, char_source_transformer_model, rnn, semi_supervised
label_desc: Use image node_id 10 for calling a global function with example usage: setup_training_model(args) and returns: task,  model,  criterion