code: def test_preprocess_copy_data_no_checks(setup, is_sparse, to_copy):
      X, y = make_regression()
      X[X < 2.5] = 0.0
  
      if is_sparse:
          X = sparse.csr_matrix(X)
          error_msg = re.escape("Does not support sparse input!")
          with pytest.raises(NotImplementedError, match=error_msg):
              X_, y_, _, _, _ = _preprocess_data(
                  X, y, True, copy=to_copy, check_input=False
              )
      else:
          X_, y_, _, _, _ = _preprocess_data(
              X, y, True, copy=to_copy, check_input=False
          )
  
          if to_copy and is_sparse:
              assert not np.may_share_memory(X_.data, X.data)
          elif to_copy:
              assert not np.may_share_memory(X_.to_numpy(), X)
          elif is_sparse:
              assert np.may_share_memory(X_.data, X.data)
  
imports: import re
  import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal, assert_almost_equal, assert_allclose
  from scipy import sparse
  from scipy import linalg
  from sklearn.datasets import make_sparse_uncorrelated, make_regression, load_iris
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model._base import make_dataset
  from sklearn.utils import check_random_state
  from ..None import LinearRegression
  from .._base import _preprocess_data, _rescale_data
label_desc: Use image node_id 16 for calling a global function with example usage: test_preprocess_copy_data_no_checks(setup, is_sparse, to_copy) without return types