code: def test_minmax_scaler_clip(setup, feature_range):
      # test behaviour of the parameter 'clip' in MinMaxScaler
      X = iris
      scaler = MinMaxScaler(feature_range=feature_range, clip=True).fit(
          X
      )
      X_min, X_max = mt.min(X, axis=0), mt.max(X, axis=0)
      X_test = [mt.r_[X_min[:2] - 10, X_max[2:] + 10]]
      X_transformed = scaler.transform(X_test)
      assert_allclose(
          X_transformed,
          [
              [
                  feature_range[0],
                  feature_range[0],
                  feature_range[1],
                  feature_range[1],
              ]
          ],
      )
  
file_path: mars/mars/learn/preprocessing/tests/test_data.py
imports: import pytest
  from sklearn.datasets import load_iris
  from sklearn.utils import gen_batches
  from sklearn.utils._testing import assert_array_almost_equal, assert_allclose
  from ....None import tensor
  from ..None import MinMaxScaler, minmax_scale
label_desc: Use image node_id 8 for calling a global function with example usage: test_minmax_scaler_clip(setup, feature_range) without return types