code: def setup_model_and_optimizer(args):
      model = get_model(args)
      tokenizer = get_tokenizer(args)
      bmt.synchronize()
      optimizer = get_optimizer(args, model)
      lr_scheduler = get_learning_rate_scheduler(args, optimizer)
      bmt.synchronize()
      optim_manager = bmt.optim.OptimManager(
          loss_scale=args.loss_scale,
          loss_scale_factor=2,
          loss_scale_steps=512,
      )
      optim_manager.add_optimizer(optimizer, lr_scheduler)
      return tokenizer, model, optimizer, lr_scheduler, optim_manager
  
imports: import json
  import time
  from typing import Any, Dict, List, Union
  import torch
  import bmtrain
  import os
  from cpm_live.arguments import get_args
  from cpm_live.models import CPMBee, CPMBeeConfig
  from cpm_live.tokenizers import CPMBeeTokenizer
  from cpm_live.utils import allgather_objects, LogManager
  from cpm_live.training_tasks.bee import MixedDataset
label_desc: Use image node_id 5 for calling a global function with example usage: setup_model_and_optimizer(args) and returns: tokenizer,  model,  optimizer,  lr_scheduler,  optim_manager