code: def test_normalize_estimator_cv():
      param_grid = {"C": [0.01]}
      a = sklearn.linear_model.LogisticRegression(
          random_state=0, solver="lbfgs"
      )
      m1 = sklearn.model_selection.GridSearchCV(a, param_grid, cv=3)
      m2 = sklearn.model_selection.GridSearchCV(a, param_grid, cv=3)
  
      assert dask.base.tokenize(m1) == dask.base.tokenize(m2)
      X, y = sklearn.datasets.make_classification()
      m1.fit(X, y)
      m2.fit(X, y)
  
      assert dask.base.tokenize(m1) == dask.base.tokenize(m2)
  
imports: import dask
  import sklearn.datasets
  import sklearn.linear_model
  import sklearn.model_selection
  import dask_ml
label_desc: Use image node_id 2 for calling a global function with example usage: test_normalize_estimator_cv() without return types