code: def model_and_optimizers(request):
      trainer_params = TrainerParameters()
  
      if request.param == 0:
          trainer_params.model = TorchNormalModel_Classification_1(
              num_labels=NormalClassificationTrainTorchConfig.num_labels,
              feature_dimension=NormalClassificationTrainTorchConfig.feature_dimension,
          )
          trainer_params.optimizers = SGD(
              trainer_params.model.parameters(), lr=0.001
          )
          dataset = TorchNormalDataset_Classification(
              num_labels=NormalClassificationTrainTorchConfig.num_labels,
              feature_dimension=NormalClassificationTrainTorchConfig.feature_dimension,
              each_label_data=NormalClassificationTrainTorchConfig.each_label_data,
              seed=NormalClassificationTrainTorchConfig.seed,
          )
          _dataloader = DataLoader(
              dataset=dataset,
              batch_size=NormalClassificationTrainTorchConfig.batch_size,
              shuffle=True,
          )
          trainer_params.train_dataloader = _dataloader
          trainer_params.evaluate_dataloaders = _dataloader
          trainer_params.metrics = {"acc": Accuracy()}
  
      elif request.param == 1:
          trainer_params.model = TorchNormalModel_Classification_1(
              num_labels=ArgMaxDatasetConfig.num_labels,
              feature_dimension=ArgMaxDatasetConfig.feature_dimension,
          )
          trainer_params.optimizers = SGD(
              trainer_params.model.parameters(), lr=0.001
          )
          dataset = TorchArgMaxDataset(
              feature_dimension=ArgMaxDatasetConfig.feature_dimension,
              data_num=ArgMaxDatasetConfig.data_num,
              seed=ArgMaxDatasetConfig.seed,
          )
          _dataloader = DataLoader(
              dataset=dataset,
              batch_size=ArgMaxDatasetConfig.batch_size,
              shuffle=True,
          )
          trainer_params.train_dataloader = _dataloader
          trainer_params.evaluate_dataloaders = _dataloader
          trainer_params.metrics = {"acc": Accuracy()}
  
      return trainer_params
  
imports: import pytest
  from dataclasses import dataclass
  from typing import Any
  from fastNLP.core.controllers.trainer import Trainer
  from fastNLP.core.samplers import BucketedBatchSampler, RandomSampler
  from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
  from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification, TorchArgMaxDataset
  from tests.helpers.callbacks.helper_callbacks import RecordLossCallback, RecordMetricCallback
  from tests.helpers.utils import magic_argv_env_context
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
label_desc: Use image node_id 1 for calling a global function with example usage: model_and_optimizers(request) and returns: trainer_params