code: def load_classification_data():
      dataset = fetch_covtype(data_home="data")
      data = np.hstack([dataset.data, dataset.target.reshape(-1, 1)])[
          :10000, :
      ]
      col_names = [f"feature_{i}" for i in range(data.shape[-1])]
      col_names[-1] = "target"
      data = pd.DataFrame(data, columns=col_names)
      data["feature_0_cat"] = pd.qcut(data["feature_0"], q=4)
      data[
          "feature_0_cat"
      ] = "feature_0_" + data.feature_0_cat.cat.codes.astype(str)
      test_idx = data.sample(
          int(0.2 * len(data)), random_state=42
      ).index
      test = data[data.index.isin(test_idx)]
      train = data[~data.index.isin(test_idx)]
      return (train, test, ["target"])
  
file_path: pytorch_tabular/tests/conftest.py
imports: import os.path
  from zipfile import ZipFile
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import fetch_california_housing, fetch_covtype
label_desc: Use image node_id 2 for calling a global function with example usage: load_classification_data() and returns: train,  test,  list