code: def test_given_imbalanced_classes_when_auto_assign_mechanism_then_handles_as_expected():
      X = np.random.normal(0, 1, 1000)
      Y = np.array(["OneClass"] * 1000)
  
      with pytest.raises(ValueError):
          assign_causal_mechanisms(
              StructuralCausalModel(nx.DiGraph([("X", "Y")])),
              pd.DataFrame({"X": X, "Y": Y}),
          )
  
      # Having at least one sample from the second class should not raise an error.
      X = np.append(X, 0)
      Y = np.append(Y, "RareClass")
  
      assign_causal_mechanisms(
          StructuralCausalModel(nx.DiGraph([("X", "Y")])),
          pd.DataFrame({"X": X, "Y": 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 26 for calling a global function with example usage: test_given_imbalanced_classes_when_auto_assign_mechanism_then_handles_as_expected() without return types