code: def trainer_params(request):
      trainer_params = TrainerParameters()
  
      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 = DataSet(
          99,
          num_features=NormalClassificationTrainTorchConfig.feature_dimension,
      )
      _dataloader = DataLoader(
          dataset=dataset,
          batch_size=NormalClassificationTrainTorchConfig.batch_size,
          shuffle=True,
      )
      trainer_params.train_dataloader = _dataloader
      trainer_params.evaluate_dataloaders = _dataloader
  
      return trainer_params
  
imports: import pytest
  from fastNLP import Metric, Evaluator
  from dataclasses import dataclass
  from typing import Any
  from itertools import product
  from fastNLP.core.controllers.trainer import Trainer
  from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
  from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification, TorchArgMaxDataset
  from tests.helpers.utils import magic_argv_env_context
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  from fastNLP import Event
label_desc: Use image node_id 1 for calling a global function with example usage: trainer_params(request) and returns: trainer_params