class_docstr: Defines the top-level Model Analysis API.
  Use RAIInsights to analyze errors, explain the most important
  features, compute counterfactuals and run causal analysis in a
  single API.
class_docstr_tok: ["Defines","the","top-level","Model","Analysis","API",".","Use","RAIInsights","to","analyze","errors",",","explain","the","most","important","features",",","compute","counterfactuals","and","run","causal","analysis","in","a","single","API","."]
docstr: Load the RAIInsights from the given path.
  
  :param path: The directory path to load the RAIInsights from.
  :type path: str
  :return: The RAIInsights object after loading.
  :rtype: RAIInsights
docstr_tok: ["Load","the","RAIInsights","from","the","given","path",".",":","param","path",":","The","directory","path","to","load","the","RAIInsights","from",".",":","type","path",":","str",":","return",":","The","RAIInsights","object","after","loading",".",":","rtype",":","RAIInsights"]
code: def load(path):
      """Load the RAIInsights from the given path.
  
      :param path: The directory path to load the RAIInsights from.
      :type path: str
      :return: The RAIInsights object after loading.
      :rtype: RAIInsights
      """
      # create the RAIInsights without any properties using the __new__
      # function, similar to pickle
      inst = RAIInsights.__new__(RAIInsights)
  
      manager_map = {
          ManagerNames.CAUSAL: CausalManager,
          ManagerNames.COUNTERFACTUAL: CounterfactualManager,
          ManagerNames.DATA_BALANCE: DataBalanceManager,
          ManagerNames.ERROR_ANALYSIS: ErrorAnalysisManager,
          ManagerNames.EXPLAINER: ExplainerManager,
      }
  
      # load current state
      RAIBaseInsights._load(
          path, inst, manager_map, RAIInsights._load_metadata
      )
      RAIInsights._load_predictions(inst, path)
      RAIInsights._load_large_data(inst, path)
  
      return inst
  
imports: import json
  import pickle
  import warnings
  from enum import Enum
  from pathlib import Path
  from typing import Any, List, Optional, Union
  import numpy
  import pandas
  from erroranalysis._internal.cohort_filter import FilterDataWithCohortFilters
  from erroranalysis._internal.process_categoricals import process_categoricals
  from raiutils.data_processing import convert_to_list
  from raiutils.exceptions import SystemErrorException, UserConfigValidationException
  from raiutils.models import Forecasting, ModelTask, SKLearn
  from responsibleai._interfaces import Dataset, RAIInsightsData, TabularDatasetMetadata
  from responsibleai._internal._forecasting_wrappers import _wrap_model
  from responsibleai._internal.constants import FileFormats, ManagerNames, Metadata, SerializationAttributes
  from responsibleai.feature_metadata import FeatureMetadata
  from responsibleai.managers.causal_manager import CausalManager
  from responsibleai.managers.counterfactual_manager import CounterfactualManager
  from responsibleai.managers.data_balance_manager import DataBalanceManager
  from responsibleai.managers.error_analysis_manager import ErrorAnalysisManager
  from responsibleai.managers.explainer_manager import ExplainerManager
  from responsibleai.rai_insights.rai_base_insights import RAIBaseInsights
label_desc: Use image node_id 38 for calling the RAIInsights obj's underlying member method code with example usage: obj.load(path) and returns: inst