code: def test_lr_score():
      X = da.from_array(np.arange(1000).reshape(1000, 1))
      lr = LinearRegression()
      lr.fit(X, X)
      assert lr.score(X, X) == pytest.approx(1, 0.001)
  
imports: import dask.array
  import dask.dataframe
  import numpy
  import numpy.linalg
  import pandas
  import pytest
  import sklearn.linear_model
  from dask.dataframe.utils import assert_eq
  from dask_glm.regularizers import Regularizer
  from sklearn.pipeline import make_pipeline
  import dask_ml.linear_model
  from dask_ml.datasets import make_classification, make_counts, make_regression
  from dask_ml.linear_model import LinearRegression, LogisticRegression, PoissonRegression
  from dask_ml.linear_model.utils import add_intercept
  from dask_ml.model_selection import GridSearchCV
label_desc: Use image node_id 18 for calling a global function with example usage: test_lr_score() without return types