code: def test_validate_multi_classification_continuous_target_column(self):
      raw_data = {
          "Column1": [10, 20, 90, 40, 50],
          "Column2": [10, 20, 90, 40, 50],
          "Target": [0.1, 0.2, 0.9, 0.4, 0.5],
      }
      data = pd.DataFrame(raw_data)
      X_data = data.drop(columns=["Target"])
      X_data[TARGET] = data["Target"].values
  
      # use valid target data to create the model
      y_train = np.array([1, 1, 2, 0, 1])
      model = create_lightgbm_classifier(X_data, y_train)
  
      with pytest.raises(
          UserConfigValidationException,
          match="Target column type must not be continuous "
          "for classification scenario.",
      ):
          RAIInsights(
              model=model,
              train=X_data,
              test=X_data,
              target_column=TARGET,
              task_type="classification",
          )
  
imports: import logging
  import random
  from unittest.mock import MagicMock
  import numpy
  import pandas
  import pytest
  from lightgbm import LGBMClassifier
  from tests.common_utils import RandomForecastingModel, RandomForecastingModelWithQuantiles, create_iris_data, create_tiny_forecasting_dataset
  from rai_test_utils.datasets.tabular import create_binary_classification_dataset, create_cancer_data, create_housing_data
  from rai_test_utils.models.lightgbm import create_lightgbm_classifier
  from rai_test_utils.models.sklearn import create_sklearn_random_forest_regressor
  from raiutils.exceptions import UserConfigValidationException
  from responsibleai import RAIInsights
  from responsibleai.feature_metadata import FeatureMetadata
  from responsibleai.rai_insights.rai_insights import _MODEL_METHOD_EXCEPTION_MESSAGE
label_desc: Use image node_id 7 for calling the TestRAIInsightsValidations obj's underlying member method code with example usage: obj.test_validate_multi_classification_continuous_target_column() without return types