class_docstr: Defines MetadataRemovalRegressionModelWrapper, wrapping the
  regression model to ignore dropped feature metadata if any.
class_docstr_tok: ["Defines","MetadataRemovalRegressionModelWrapper",",","wrapping","the","regression","model","to","ignore","dropped","feature","metadata","if","any","."]
code: def predict(self, dataset: pd.DataFrame):
      if (
          self.dropped_features is None
          or len(self.dropped_features) == 0
      ):
          return self.model.predict(dataset)
      return self.model.predict(
          dataset.drop(columns=self.dropped_features, axis=1)
      )
  
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 2 for calling the MetadataRemovalRegressionModelWrapper obj's underlying member method code with example usage: obj.predict(dataset) and returns: self, self