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: Get the name of the attribute representing the model's output.
  
  :param purpose: the purpose to identify suitable model methods
  :type purpose: MethodPurpose
  :param large: whether or not the output is based on a large (full)
      dataset
  :type large: boolean
  :return: the attribute's name
  :rtype: str
docstr_tok: ["Get","the","name","of","the","attribute","representing","the","model","'s","output",".",":","param","purpose",":","the","purpose","to","identify","suitable","model","methods",":","type","purpose",":","MethodPurpose",":","param","large",":","whether","or","not","the","output","is","based","on","a","large","(","full",")","dataset",":","type","large",":","boolean",":","return",":","the","attribute","'s","name",":","rtype",":","str"]
code: def _get_model_output_name(self, *, purpose, large=False):
      """Get the name of the attribute representing the model's output.
  
      :param purpose: the purpose to identify suitable model methods
      :type purpose: MethodPurpose
      :param large: whether or not the output is based on a large (full)
          dataset
      :type large: boolean
      :return: the attribute's name
      :rtype: str
      """
      model_method = self._get_model_method(purpose=purpose)
      return (
          f"_{'large_' if large else ''}{model_method.__name__}_output"
      )
  
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 31 for calling the RAIInsights obj's underlying member method code with example usage: obj._get_model_output_name() and returns: str, unknown, model_method, str