code: def test_model_coef_dask_numpy(est, data, request):
      """Tests that models return same coefficients and intercepts with array types"""
      X, y = request.getfixturevalue(data)
      np_mod, da_mod = est(fit_intercept=True), est(fit_intercept=True)
      da_mod.fit(X, y)
      np_mod.fit(X.compute(), y.compute())
      da_coef = np.hstack((da_mod.coef_, da_mod.intercept_))
      np_coef = np.hstack((np_mod.coef_, np_mod.intercept_))
  
      rel_error = LA.norm(da_coef - np_coef) / LA.norm(np_coef)
      assert rel_error < 1e-8
  
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 21 for calling a global function with example usage: test_model_coef_dask_numpy(est, data, request) without return types