code: def _assert_predict_dict_identical_to_predict(
      predictor: TabularPredictor, data
  ):
      """Assert that predict_multi is identical to looping calls to predict"""
      for as_pandas in [True, False]:
          for inverse_transform in [True, False]:
              predict_dict = predictor.predict_multi(
                  data=data,
                  as_pandas=as_pandas,
                  inverse_transform=inverse_transform,
              )
              assert set(predictor.model_names()) == set(
                  predict_dict.keys()
              )
              for m in predictor.model_names():
                  if not inverse_transform:
                      model_pred = predictor._learner.predict(
                          data,
                          model=m,
                          as_pandas=as_pandas,
                          inverse_transform=inverse_transform,
                      )
                  else:
                      model_pred = predictor.predict(
                          data, model=m, as_pandas=as_pandas
                      )
                  if as_pandas:
                      assert model_pred.equals(predict_dict[m])
                  else:
                      assert np.array_equal(model_pred, predict_dict[m])
  
imports: import os
  import shutil
  import sys
  import tempfile
  import warnings
  from random import seed
  import numpy
  import pandas
  import pytest
  from networkx.exception import NetworkXError
  from autogluon.common import space
  from autogluon.common.utils.simulation_utils import convert_simulation_artifacts_to_tabular_predictions_dict
  from autogluon.core.constants import BINARY, MULTICLASS, PROBLEM_TYPES_CLASSIFICATION, QUANTILE, REGRESSION
  from autogluon.core.utils import download, unzip
  from autogluon.tabular import TabularDataset, TabularPredictor
  from autogluon.tabular.configs.hyperparameter_configs import get_hyperparameter_config
label_desc: Use image node_id 2 for calling a global function with example usage: _assert_predict_dict_identical_to_predict(predictor, data) without return types