code: def _make_regression_data(
      dims: Tuple[int, ...] = (100, 3),
      types: Tuple[
          np.ndarray | csr_matrix | pd.DataFrame | list,
          np.ndarray | csr_matrix | pd.DataFrame | list | pd.Series,
      ] = (np.ndarray, np.ndarray),
      random_state: int | np.random.RandomState = DEFAULT_SEED,
  ) -> Data:
      X_type, y_type = types
      rs = check_random_state(random_state)
  
      if X_type == csr_matrix:
          X = rs.choice([0, 1], dims)
      else:
          X = rs.rand(*dims)
  
      y = rs.rand(dims[0])
  
      return astype(X_type, X), astype(y_type, y)
  
file_path: auto-sklearn/test/fixtures/datasets.py
imports: from __future__ import annotations
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple
  import numpy
  import pandas
  from scipy.sparse import csr_matrix
  from sklearn.utils import check_random_state
  from autosklearn.constants import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, MULTIOUTPUT_REGRESSION, REGRESSION
  from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES
  from autosklearn.data.xy_data_manager import XYDataManager
  from autosklearn.pipeline.util import get_dataset
  from pytest import fixture
  from test.conftest import DEFAULT_SEED
label_desc: Use image node_id 6 for calling a global function with example usage: _make_regression_data(dims, types, random_state) and returns: astype,  astype