code: def test_cv_sample_weight(iris_train):
      X, y, feature_names, target_names = iris_train
      weights_ones = np.ones(len(y))
      model = RandomForestClassifier(random_state=42)
  
      # we expect no exception to be raised when passing weights with a CV
      perm_weights = PermutationImportance(
          model, cv=5, random_state=42
      ).fit(X, y, sample_weight=weights_ones)
      perm = PermutationImportance(model, cv=5, random_state=42).fit(
          X, y
      )
  
      # passing a vector of weights filled with one should be the same as passing no weights
      assert (
          perm.feature_importances_ == perm_weights.feature_importances_
      ).all()
  
imports: import pytest
  import numpy
  from sklearn.base import is_classifier, is_regressor
  from sklearn.svm import SVR, SVC
  from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
  from sklearn.model_selection import train_test_split, cross_val_score
  from sklearn.pipeline import make_pipeline
  from sklearn.feature_selection import SelectFromModel
  from sklearn.linear_model import LogisticRegression
  import eli5
  from eli5.sklearn import PermutationImportance
  from .utils import format_as_all
label_desc: Use image node_id 13 for calling a global function with example usage: test_cv_sample_weight(iris_train) without return types