code: def test_lm(fit_intercept):
      X, y = make_regression(n_samples=100, n_features=5, chunks=50)
      lr = LinearRegression(fit_intercept=fit_intercept)
      lr.fit(X, y)
      lr.predict(X)
      if fit_intercept:
          assert lr.intercept_ is not None
  
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 7 for calling a global function with example usage: test_lm(fit_intercept) without return types