class_docstr: Defines the ExplainerManager for explaining a model.
class_docstr_tok: ["Defines","the","ExplainerManager","for","explaining","a","model","."]
docstr: Load the ExplainerManager from the given path.
  
  :param path: The directory path to load the ExplainerManager from.
  :type path: str
  :param rai_insights: The loaded parent RAIInsights.
  :type rai_insights: RAIInsights
  :return: The ExplainerManager manager after loading.
  :rtype: ExplainerManager
docstr_tok: ["Load","the","ExplainerManager","from","the","given","path",".",":","param","path",":","The","directory","path","to","load","the","ExplainerManager","from",".",":","type","path",":","str",":","param","rai_insights",":","The","loaded","parent","RAIInsights",".",":","type","rai_insights",":","RAIInsights",":","return",":","The","ExplainerManager","manager","after","loading",".",":","rtype",":","ExplainerManager"]
code: def _load(path, rai_insights):
      """Load the ExplainerManager from the given path.
  
      :param path: The directory path to load the ExplainerManager from.
      :type path: str
      :param rai_insights: The loaded parent RAIInsights.
      :type rai_insights: RAIInsights
      :return: The ExplainerManager manager after loading.
      :rtype: ExplainerManager
      """
      # create the ExplainerManager without any properties using the __new__
      # function, similar to pickle
      inst = ExplainerManager.__new__(ExplainerManager)
  
      all_cf_dirs = DirectoryManager.list_sub_directories(path)
      if len(all_cf_dirs) != 0:
          directory_manager = DirectoryManager(
              parent_directory_path=path,
              sub_directory_name=all_cf_dirs[0],
          )
          data_directory = directory_manager.get_data_directory()
  
          with open(
              data_directory / Metadata.META_JSON, "r"
          ) as meta_file:
              meta = meta_file.read()
          meta = json.loads(meta)
          inst.__dict__["_" + IS_RUN] = meta[IS_RUN]
          inst.__dict__["_" + IS_ADDED] = meta[IS_ADDED]
  
          inst.__dict__[EXPLANATION] = None
          explanation_path = data_directory / ManagerNames.EXPLAINER
          if explanation_path.exists():
              explanation = load_explanation(explanation_path)
              inst.__dict__[EXPLANATION] = explanation
      else:
          inst.__dict__["_" + IS_RUN] = False
          inst.__dict__["_" + IS_ADDED] = False
          inst.__dict__[EXPLANATION] = None
  
      inst.__dict__["_" + Metadata.MODEL] = rai_insights.model
      inst.__dict__["_" + CLASSES] = rai_insights._classes
      inst.__dict__[
          "_" + CATEGORICAL_FEATURES
      ] = rai_insights.get_categorical_features_after_drop()
      target_column = rai_insights.target_column
      train = rai_insights.get_train_data().drop(
          columns=[target_column]
      )
      test = rai_insights.get_test_data().drop(columns=[target_column])
      inst.__dict__[U_INITIALIZATION_EXAMPLES] = train
      inst.__dict__[U_EVALUATION_EXAMPLES] = test
      inst.__dict__["_" + FEATURES] = list(train.columns)
  
      # reset the surrogate model
      inst._initialize_surrogate_model()
  
      return inst
  
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 13 for calling the ExplainerManager obj's underlying member method code with example usage: obj._load(path, rai_insights) and returns: inst