code: def test_classification(
      classification_data,
      continuous_cols,
      categorical_cols,
      continuous_feature_transform,
      normalize_continuous_features,
  ):
      (train, test, target) = classification_data
      if len(continuous_cols) + len(categorical_cols) == 0:
          return
  
      data_config = DataConfig(
          target=target,
          continuous_cols=continuous_cols,
          categorical_cols=categorical_cols,
          continuous_feature_transform=continuous_feature_transform,
          normalize_continuous_features=normalize_continuous_features,
      )
      model_config_params = {"task": "classification"}
      model_config = CategoryEmbeddingModelConfig(**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=train)
  
      result = tabular_model.evaluate(test)
      # print(result[0]["valid_loss"])
      assert "test_accuracy" in result[0].keys()
      pred_df = tabular_model.predict(test)
      assert pred_df.shape[0] == test.shape[0]
  
file_path: pytorch_tabular/tests/test_categorical_embedding.py
imports: import numpy
  import pytest
  import torch
  from pytorch_tabular import TabularModel
  from pytorch_tabular.categorical_encoders import CategoricalEmbeddingTransformer
  from pytorch_tabular.config import DataConfig, OptimizerConfig, TrainerConfig
  from pytorch_tabular.models import CategoryEmbeddingModelConfig
  from sklearn.preprocessing import PowerTransformer
label_desc: Use image node_id 3 for calling a global function with example usage: test_classification(classification_data, continuous_cols, categorical_cols, continuous_feature_transform, normalize_continuous_features) without return types