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=0.01
          )
          dataset = TorchNormalDataset_Classification(
              num_labels=NormalClassificationTrainTorchConfig.num_labels,
              feature_dimension=NormalClassificationTrainTorchConfig.feature_dimension,
              each_label_data=NormalClassificationTrainTorchConfig.each_label_data,
              seed=NormalClassificationTrainTorchConfig.seed,
          )
          trainer_params.train_dataloader = DataLoader(
              dataset=dataset,
              batch_size=NormalClassificationTrainTorchConfig.batch_size,
              shuffle=True,
          )
          trainer_params.evaluate_dataloaders = None
          trainer_params.input_mapping = None
          trainer_params.output_mapping = None
  
      # elif request.param == 1:
  
      return trainer_params
  
imports: import os.path
  import subprocess
  import sys
  import pytest
  from dataclasses import dataclass
  from typing import Any
  from pathlib import Path
  from fastNLP.core.controllers.trainer import Trainer
  from tests.helpers.models.torch_model import TorchNormalModel_Classification_1, TorchNormalModel_Classification_3
  from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification
  from tests.helpers.callbacks.helper_callbacks import RecordLossCallback
  from tests.helpers.callbacks.helper_callbacks_torch import RecordAccumulationStepsCallback_Torch
  from tests.helpers.utils import magic_argv_env_context, Capturing
  from fastNLP.envs.distributed import rank_zero_rm
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH, _TORCH_GREATER_EQUAL_1_12
label_desc: Use image node_id 1 for calling a global function with example usage: model_and_optimizers(request) and returns: trainer_params