code: def _run_tta(
      model_config_class,
      model_config_params,
      data_config,
      train,
      test,
      aggregate,
  ):
      model_config = model_config_class(**model_config_params)
      trainer_config = TrainerConfig(
          max_epochs=3,
          checkpoints=None,
          early_stopping=None,
          accelerator="cpu",
          fast_dev_run=True,
      )
      optimizer_config = OptimizerConfig()
  
      tabular_model = TabularModel(
          data_config=data_config,
          model_config=model_config,
          optimizer_config=optimizer_config,
          trainer_config=trainer_config,
      )
      tabular_model.fit(train)
      pred_df = tabular_model.predict(
          test,
          test_time_augmentation=True,
          num_tta=2,
          aggregate_tta=aggregate,
      )
      return pred_df
  
imports: import copy
  import os
  import numpy
  import pytest
  import torch
  from pytorch_tabular import TabularModel, TabularModelTuner, model_sweep
  from pytorch_tabular.config import DataConfig, OptimizerConfig, TrainerConfig
  from pytorch_tabular.config.config import SSLModelConfig
  from pytorch_tabular.feature_extractor import DeepFeatureExtractor
  from pytorch_tabular.models import AutoIntConfig, CategoryEmbeddingModelConfig, FTTransformerConfig, GANDALFConfig, GatedAdditiveTreeEnsembleConfig, NodeConfig, TabNetModelConfig
  from pytorch_tabular.ssl_models import DenoisingAutoEncoderConfig
  from scipy.stats import randint, uniform
  from sklearn.metrics import accuracy_score, r2_score
  from sklearn.model_selection import KFold
label_desc: Use image node_id 18 for calling a global function with example usage: _run_tta(model_config_class, model_config_params, data_config, train, test, aggregate) and returns: pred_df