code: def get_wrapped_model(model, dropped_features):
      predict_proba_flag = hasattr(model, "predict_proba")
      if predict_proba_flag:
          wrapper_model = MetadataRemovalClassificationModelWrapper(
              model, dropped_features
          )
      else:
          wrapper_model = MetadataRemovalRegressionModelWrapper(
              model, dropped_features
          )
      return wrapper_model
  
imports: import json
  import warnings
  from pathlib import Path
  from typing import Any, List, Optional
  import jsonschema
  import pandas
  from erroranalysis._internal.constants import metric_to_display_name
  from erroranalysis._internal.error_analyzer import ModelAnalyzer
  from erroranalysis._internal.error_report import as_error_report
  from erroranalysis._internal.error_report import json_converter
  from raiutils.exceptions import UserConfigValidationException
  from raiutils.models import ModelTask
  from responsibleai._config.base_config import BaseConfig
  from responsibleai._interfaces import ErrorAnalysisData
  from responsibleai._internal.constants import ErrorAnalysisManagerKeys
  from responsibleai._internal.constants import FileFormats, ListProperties, ManagerNames
  from responsibleai._tools.shared.state_directory_management import DirectoryManager
  from responsibleai.exceptions import ConfigAndResultMismatchException, DuplicateManagerConfigException
  from responsibleai.managers.base_manager import BaseManager
  from responsibleai.utils import _find_features_having_missing_values, _measure_time
label_desc: Use image node_id 3 for calling a global function with example usage: get_wrapped_model(model, dropped_features) and returns: wrapper_model