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 predictions.
  
  :param inst: RAIInsights object instance.
  :type inst: RAIInsights
  :param path: The directory path to data location.
  :type path: str
docstr_tok: ["Load","the","predictions",".",":","param","inst",":","RAIInsights","object","instance",".",":","type","inst",":","RAIInsights",":","param","path",":","The","directory","path","to","data","location",".",":","type","path",":","str"]
code: def _load_predictions(inst, path):
      """Load the predictions.
  
      :param inst: RAIInsights object instance.
      :type inst: RAIInsights
      :param path: The directory path to data location.
      :type path: str
      """
      if inst.__dict__[_MODEL] is None:
          for data_name in _OUTPUT_FIELDS:
              inst.__dict__[data_name] = None
          return
  
      prediction_output_path = (
          Path(path) / SerializationAttributes.PREDICTIONS_DIRECTORY
      )
  
      for data_name, file_name in _OUTPUT_FIELDS_AND_FILENAMES:
          file_path = prediction_output_path / file_name
          if file_path.exists():
              with open(file_path, "r") as file:
                  inst.__dict__[data_name] = np.array(json.load(file))
          else:
              inst.__dict__[data_name] = None
  
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 36 for calling the RAIInsights obj's underlying member method code with example usage: obj._load_predictions(inst, path) without return types