code: def _generate_linear_classification_data():
      X = np.random.normal(0, 1, (1000, 5))
      Y = (
          np.sum(X * np.random.uniform(-5, 5, X.shape[1]), axis=1) > 0
      ).astype(str)
  
      return 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 3 for calling a global function with example usage: _generate_linear_classification_data() and returns: X,  Y