code: def test_obvious_cases_one(random_xy_dataset_regr, chunks):
      X, y = random_xy_dataset_regr
      y = np.ones(y.shape)
      x_transform = (
          IntervalEncoder(n_chunks=chunks).fit(X, y).transform(X)
      )
      assert x_transform.shape == X.shape
      assert np.all(np.isclose(x_transform, 1.0))
  
file_path: scikit-lego/tests/test_preprocessing/test_interval_encoder.py
imports: import numpy
  import pytest
  from sklearn.utils import estimator_checks
  from sklego.common import flatten
  from sklego.preprocessing import IntervalEncoder
  from tests.conftest import general_checks, transformer_checks
label_desc: Use image node_id 2 for calling a global function with example usage: test_obvious_cases_one(random_xy_dataset_regr, chunks) without return types