class_docstr: Defines the top-level RAIVisionInsights API.
  
  Use RAIVisionInsights to assess vision machine learning models in a
  single API.
class_docstr_tok: ["Defines","the","top-level","RAIVisionInsights","API",".","Use","RAIVisionInsights","to","assess","vision","machine","learning","models","in","a","single","API","."]
docstr: Load the RAIVisionInsights from the given path.
  
  :param path: The directory path to load the RAIVisionInsights from.
  :type path: str
  :return: The RAIVisionInsights object after loading.
  :rtype: RAIVisionInsights
docstr_tok: ["Load","the","RAIVisionInsights","from","the","given","path",".",":","param","path",":","The","directory","path","to","load","the","RAIVisionInsights","from",".",":","type","path",":","str",":","return",":","The","RAIVisionInsights","object","after","loading",".",":","rtype",":","RAIVisionInsights"]
code: def load(path):
      """Load the RAIVisionInsights from the given path.
  
      :param path: The directory path to load the RAIVisionInsights from.
      :type path: str
      :return: The RAIVisionInsights object after loading.
      :rtype: RAIVisionInsights
      """
      # create the RAIVisionInsights without any properties using the __new__
      # function, similar to pickle
      inst = RAIVisionInsights.__new__(RAIVisionInsights)
  
      manager_map = {
          ManagerNames.EXPLAINER: ExplainerManager,
          ManagerNames.ERROR_ANALYSIS: ErrorAnalysisManager,
      }
  
      # load current state
      RAIBaseInsights._load(
          path, inst, manager_map, RAIVisionInsights._load_metadata
      )
      inst._wrapped_model = wrap_model(
          inst.model,
          inst.test,
          inst.task_type,
          classes=inst._classes,
          device=inst.device,
      )
      inst.automl_image_model = is_automl_image_model(
          inst._wrapped_model
      )
      inst.predict_output = None
      return inst
  
imports: import base64
  import io
  import json
  import os
  import pickle
  import shutil
  import warnings
  from collections import defaultdict
  from enum import Enum
  from pathlib import Path
  from typing import Any, Optional
  import matplotlib.pyplot
  import numpy
  import pandas
  import torch
  from ml_wrappers import wrap_model
  from ml_wrappers.common.constants import Device
  from torchmetrics.detection.mean_ap import MeanAveragePrecision
  from vision_explanation_methods.error_labeling.error_labeling import ErrorLabeling, ErrorLabelType
  from erroranalysis._internal.cohort_filter import FilterDataWithCohortFilters
  from raiutils.data_processing import convert_to_list
  from raiutils.models.model_utils import SKLearn
  from responsibleai._interfaces import Dataset, RAIInsightsData
  from responsibleai._internal.constants import ManagerNames, Metadata, SerializationAttributes
  from responsibleai.exceptions import UserConfigValidationException
  from responsibleai.feature_metadata import FeatureMetadata
  from responsibleai.rai_insights.rai_base_insights import RAIBaseInsights
  from responsibleai.serialization_utilities import serialize_json_safe
  from responsibleai_vision.common.constants import CommonTags, ExplainabilityDefaults, ImageColumns, ImageModes, MLFlowSchemaLiterals, ModelTask
  from responsibleai_vision.managers.error_analysis_manager import ErrorAnalysisManager
  from responsibleai_vision.managers.explainer_manager import ExplainerManager
  from responsibleai_vision.utils.feature_extractors import extract_features
  from responsibleai_vision.utils.image_reader import get_base64_string_from_path, get_image_from_path, is_automl_image_model
  from responsibleai_vision.utils.image_utils import convert_images, get_images, transform_object_detection_labels
label_desc: Use image node_id 28 for calling the RAIVisionInsights obj's underlying member method code with example usage: obj.load(path) and returns: inst