class_docstr: Defines the ExplainerManager for explaining a model.
class_docstr_tok: ["Defines","the","ExplainerManager","for","explaining","a","model","."]
docstr: Return the explanations for a given point(s) .
  
  :param local: True if local explanations are requested
                and False otherwise.
  :type local: bool
  :param data: The data point(s) for which the explanations
               need to be generated.
  :type data: Any
  :return: The explanations for the given data point(s)
           according to the interface specified by
           ModelExplanationData.
  :rtype: ModelExplanationData
docstr_tok: ["Return","the","explanations","for","a","given","point","(","s",")",".",":","param","local",":","True","if","local","explanations","are","requested","and","False","otherwise",".",":","type","local",":","bool",":","param","data",":","The","data","point","(","s",")","for","which","the","explanations","need","to","be","generated",".",":","type","data",":","Any",":","return",":","The","explanations","for","the","given","data","point","(","s",")","according","to","the","interface","specified","by","ModelExplanationData",".",":","rtype",":","ModelExplanationData"]
code: def request_explanations(self, local: bool, data: Any):
      """Return the explanations for a given point(s) .
  
      :param local: True if local explanations are requested
                    and False otherwise.
      :type local: bool
      :param data: The data point(s) for which the explanations
                   need to be generated.
      :type data: Any
      :return: The explanations for the given data point(s)
               according to the interface specified by
               ModelExplanationData.
      :rtype: ModelExplanationData
      """
      if not isinstance(data, pd.DataFrame):
          raise UserConfigValidationException(
              "Data is of type {0} but it must be "
              "a pandas DataFrame.".format(type(data))
          )
  
      if local and data.shape[0] > 1:
          raise UserConfigValidationException(
              "Only one row of data is allowed for "
              "local explanation generation."
          )
  
      explanations = self._compute_explanations(local=local, data=data)
      return self._get_interpret(explanations, data)
  
imports: import json
  import warnings
  from pathlib import Path
  from typing import Any, List, Optional
  import numpy
  import pandas
  from interpret_community.explanation.explanation import FeatureImportanceExplanation, load_explanation, save_explanation
  from interpret_community.mimic.mimic_explainer import MimicExplainer
  from interpret_community.mimic.models.lightgbm_model import LGBMExplainableModel
  from interpret_community.mimic.models.linear_model import LinearExplainableModel
  from scipy.sparse import issparse
  from raiutils.data_processing import convert_to_list
  from raiutils.exceptions import UserConfigValidationException
  from raiutils.models import ModelTask
  from responsibleai._interfaces import EBMGlobalExplanation, FeatureImportance, ModelExplanationData, PrecomputedExplanations
  from responsibleai._internal.constants import ExplainerManagerKeys
  from responsibleai._internal.constants import ExplanationKeys, ListProperties, ManagerNames, Metadata
  from responsibleai._tools.shared.state_directory_management import DirectoryManager
  from responsibleai.managers.base_manager import BaseManager
  from responsibleai.utils import _find_features_having_missing_values, _measure_time
label_desc: Use image node_id 9 for calling the ExplainerManager obj's underlying member method code with example usage: obj.request_explanations(local, data) and returns: self