code: def validate_rai_insights(
      rai_insights,
      train_data,
      test_data,
      target_column,
      task_type,
      categorical_features,
      feature_metadata=None,
  ):
      pd.testing.assert_frame_equal(rai_insights.train, train_data)
      pd.testing.assert_frame_equal(rai_insights.test, test_data)
      assert rai_insights.target_column == target_column
      assert rai_insights.task_type == task_type
      assert rai_insights.categorical_features == (
          categorical_features or []
      )
      expected_length = 0
      if categorical_features is not None:
          expected_length = len(categorical_features)
      assert len(rai_insights.categorical_features) == expected_length
      assert len(rai_insights._categories) == expected_length
      assert len(rai_insights._categorical_indexes) == expected_length
      assert len(rai_insights._category_dictionary) == expected_length
      for ind_data in rai_insights._string_ind_data:
          assert len(ind_data) == expected_length
  
      if feature_metadata is not None:
          if (
              feature_metadata.dropped_features is not None
              and categorical_features is not None
          ):
              assert len(
                  rai_insights.get_categorical_features_after_drop()
              ) == len(categorical_features) - len(
                  feature_metadata.dropped_features
              )
      else:
          if categorical_features is not None:
              assert len(
                  rai_insights.get_categorical_features_after_drop()
              ) == len(categorical_features)
          else:
              assert (
                  len(
                      rai_insights.get_categorical_features_after_drop()
                  )
                  == 0
              )
  
      if rai_insights.model is None:
          assert rai_insights._predict_output is None
          assert rai_insights._predict_proba_output is None
      else:
          assert rai_insights._predict_output is not None
          if task_type == ModelTask.CLASSIFICATION:
              assert rai_insights._predict_proba_output is not None
              assert isinstance(
                  rai_insights._predict_proba_output, np.ndarray
              )
              assert len(
                  rai_insights._predict_proba_output.tolist()[0]
              ) == len(rai_insights._classes)
  
      if task_type == ModelTask.CLASSIFICATION:
          classes = train_data[target_column].unique()
          classes.sort()
          np.testing.assert_array_equal(rai_insights._classes, classes)
  
imports: import os
  import pickle
  from pathlib import Path
  from tempfile import TemporaryDirectory
  import numpy
  import pandas
  import pytest
  from tests.causal_manager_validator import validate_causal
  from tests.common_utils import create_iris_data
  from tests.counterfactual_manager_validator import validate_counterfactual
  from tests.error_analysis_validator import setup_error_analysis, validate_error_analysis
  from tests.explainer_manager_validator import setup_explainer, validate_explainer
  from rai_test_utils.datasets.tabular import create_binary_classification_dataset, create_cancer_data, create_housing_data
  from rai_test_utils.models.model_utils import create_models_classification, create_models_regression
  from rai_test_utils.models.sklearn import create_complex_classification_pipeline
  from rai_test_utils.utilities import is_valid_uuid
  from raiutils.models import ModelTask
  from responsibleai import RAIInsights
  from responsibleai._internal.constants import ManagerNames, SerializationAttributes
  from responsibleai._tools.shared.state_directory_management import DirectoryManager
  from responsibleai.feature_metadata import FeatureMetadata
label_desc: Use image node_id 4 for calling a global function with example usage: validate_rai_insights(rai_insights, train_data, test_data, target_column, task_type, categorical_features, feature_metadata) without return types