code: def test_given_categorical_training_data_with_many_categories_when_fit_classification_model_then_returns_reasonably_accurate_predictions():
      def _generate_data():
          X = np.column_stack(
              [
                  np.random.choice(20, 1000, replace=True).astype(str),
                  np.random.normal(0, 1, (1000, 2)).astype(object),
              ]
          ).astype(object)
          Y = []
  
          for x in X:
              if int(x[0]) % 2 == 0:
                  if x[1] * x[2] < 1:
                      Y.append("Class 0")
                  elif x[1] * x[2] > 1:
                      Y.append("Class 1")
                  else:
                      Y.append("Class 2")
              else:
                  if x[1] + x[2] < 1:
                      Y.append("Class 2")
                  elif x[1] + x[2] > 1:
                      Y.append("Class 1")
                  else:
                      Y.append("Class 0")
  
          return X, np.array(Y)
  
      X_training, Y_training = _generate_data()
      X_test, Y_test = _generate_data()
      mdl = create_hist_gradient_boost_classifier()
      mdl.fit(X_training, Y_training)
  
      assert np.sum(mdl.predict(X_test).reshape(-1) == Y_test) > 950
  
imports: import numpy
  from flaky import flaky
  from dowhy.gcm.ml import create_hist_gradient_boost_classifier, create_polynom_logistic_regression_classifier
  from dowhy.gcm.ml.classification import create_support_vector_classifier
label_desc: Use image node_id 2 for calling a global function with example usage: test_given_categorical_training_data_with_many_categories_when_fit_classification_model_then_returns_reasonably_accurate_predictions() and returns: X,  np