code: def build_trainer(
      opt, device_id, model, vocabs, optim, model_saver=None
  ):
      """
      Simplify `Trainer` creation based on user `opt`s*
  
      Args:
          opt (:obj:`Namespace`): user options (usually from argument parsing)
          model (:obj:`onmt.models.NMTModel`): the model to train
          fields (dict): dict of fields
          optim (:obj:`onmt.utils.Optimizer`): optimizer used during training
          data_type (str): string describing the type of data
              e.g. "text"
          model_saver(:obj:`onmt.models.ModelSaverBase`): the utility object
              used to save the model
      """
  
      train_loss = LossCompute.from_opts(opt, model, vocabs["tgt"])
      valid_loss = LossCompute.from_opts(
          opt, model, vocabs["tgt"], train=False
      )
  
      scoring_preparator = ScoringPreparator(vocabs=vocabs, opt=opt)
      validset_transforms = opt.data.get("valid", {}).get(
          "transforms", None
      )
      if validset_transforms:
          scoring_preparator.warm_up(validset_transforms)
      scorers_cls = get_scorers_cls(opt.valid_metrics)
      valid_scorers = build_scorers(opt, scorers_cls)
  
      trunc_size = opt.truncated_decoder  # Badly named...
      norm_method = opt.normalization
      accum_count = opt.accum_count
      accum_steps = opt.accum_steps
      n_gpu = opt.world_size
      parallel_mode = opt.parallel_mode
      average_decay = opt.average_decay
      average_every = opt.average_every
      dropout = opt.dropout
      attention_dropout = opt.attention_dropout
      dropout_steps = opt.dropout_steps
      zero_out_prompt_loss = opt.zero_out_prompt_loss
      if device_id >= 0:
          gpu_rank = opt.gpu_ranks[device_id]
      else:
          gpu_rank = -1
          n_gpu = 0
  
      earlystopper = (
          onmt.utils.EarlyStopping(
              opt.early_stopping,
              scorers=onmt.utils.scorers_from_opts(opt),
          )
          if opt.early_stopping > 0
          else None
      )
  
      report_manager = onmt.utils.build_report_manager(opt, gpu_rank)
      trainer = Trainer(
          model,
          train_loss,
          valid_loss,
          scoring_preparator,
          valid_scorers,
          optim,
          trunc_size,
          norm_method,
          accum_count,
          accum_steps,
          n_gpu,
          gpu_rank,
          parallel_mode,
          report_manager,
          with_align=True if opt.lambda_align > 0 else False,
          model_saver=model_saver,
          average_decay=average_decay,
          average_every=average_every,
          model_dtype=opt.model_dtype,
          earlystopper=earlystopper,
          dropout=dropout,
          attention_dropout=attention_dropout,
          dropout_steps=dropout_steps,
          zero_out_prompt_loss=zero_out_prompt_loss,
      )
      return trainer
  
imports: import time
  import sys
  import torch
  import traceback
  import onmt.utils
  from onmt.utils.loss import LossCompute
  from onmt.utils.logging import logger
  from onmt.utils.scoring_utils import ScoringPreparator
  from onmt.scorers import get_scorers_cls, build_scorers
label_desc: Use image node_id 1 for calling a global function with example usage: build_trainer(opt, device_id, model, vocabs, optim, model_saver) and returns: trainer