code: def initialize_torch(
      model,
      dataset,
      tokenizer,
      cfg_train,
      cfg_impl,
      elapsed_time,
      setup=_default_setup,
  ):
      """initialize a torch engine."""
      if dataset is not None:
          dataloader = prepare_pretraining_dataloader(
              dataset, tokenizer, cfg_train, cfg_impl
          )
      else:
          dataloader = None
  
      # in most cases we can use a simpler Engine class:
      require_full_engine = (
          "sequence_curriculum" in cfg_train
          or "weight_averaging" in cfg_train
          or "gradinit" in cfg_train
      )
  
      if require_full_engine:
          model_engine = TorchEngineFull(
              model,
              cfg_train,
              cfg_impl,
              elapsed_time,
              setup=setup,
              seq_length=tokenizer.model_max_length,
          )
      else:
          model_engine = TorchEngineMinimal(
              model,
              cfg_train,
              cfg_impl,
              elapsed_time,
              setup=setup,
              seq_length=tokenizer.model_max_length,
          )
      model_engine.train()  # This is the default engine state. Pretraining scripts may change this.
      return (
          model_engine,
          model_engine.optimizer,
          model_engine.scheduler,
          dataloader,
      )
  
imports: import torch
  import torch._inductor.utils
  import os
  import json
  from omegaconf import OmegaConf
  from functools import partial
  from contextlib import nullcontext
  import time
  import logging
  import transformers
  from safetensors.torch import load_file, save_file
  from transformers.utils.generic import working_or_temp_dir
  from torch.distributed.optim import ZeroRedundancyOptimizer
  from .utils import group_parameters, prepare_pretraining_dataloader, update_ema, updated_latest_weight_average
  from .optimizers.schedulers import get_schedule_fn
  from .optimizers import Adahessian, AdamWScale, Shampoo, LARS, SAM, ProgressiveBatching, AGD, Sophia
  import warnings
label_desc: Use image node_id 1 for calling a global function with example usage: initialize_torch(model, dataset, tokenizer, cfg_train, cfg_impl, elapsed_time, setup) and returns: model_engine,  model_engine,  model_engine,  dataloader