code: def test_given_data_with_rare_categorical_features_when_calling_has_linear_relationship_then_does_not_raise_exception():
      X = np.array(["Feature" + str(i) for i in range(20)])
      Y = np.append(
          np.array(["Class1"] * 10), np.array(["Class2"] * 10)
      )
  
      assert has_linear_relationship(X, Y)
  
imports: import networkx
  import numpy
  import pandas
  import pytest
  from _pytest.python_api import approx
  from flaky import flaky
  from pytest import mark
  from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
  from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV
  from sklearn.naive_bayes import GaussianNB
  from sklearn.pipeline import Pipeline
  from dowhy.gcm import AdditiveNoiseModel, DiscreteAdditiveNoiseModel, EmpiricalDistribution, ProbabilisticCausalModel, StructuralCausalModel, draw_samples, fit
  from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms, has_linear_relationship
label_desc: Use image node_id 23 for calling a global function with example usage: test_given_data_with_rare_categorical_features_when_calling_has_linear_relationship_then_does_not_raise_exception() without return types