code: def test_when_fit_called_then_models_train_and_returned_predictor_inference_has_mean_and_correct_quantiles(
      model_class, quantile_levels, temp_model_path
  ):
      model = model_class(
          path=temp_model_path,
          freq="H",
          prediction_length=3,
          quantile_levels=quantile_levels,
          hyperparameters=DUMMY_HYPERPARAMETERS,
      )
      model.fit(train_data=DUMMY_TS_DATAFRAME)
      predictions = model.predict(
          DUMMY_TS_DATAFRAME, quantile_levels=quantile_levels
      )
  
      assert isinstance(predictions, TimeSeriesDataFrame)
  
      predicted_item_index = predictions.item_ids
      expected_columns = ["mean"] + [str(q) for q in quantile_levels]
      assert all(
          predicted_item_index == DUMMY_TS_DATAFRAME.item_ids
      )  # noqa
      assert (predictions.columns == expected_columns).all()
  
imports: import itertools
  import shutil
  import sys
  import tempfile
  from unittest import mock
  import numpy
  import pandas
  import pytest
  from flaky import flaky
  from autogluon.common import space
  from autogluon.core.hpo.constants import RAY_BACKEND
  from autogluon.timeseries import TimeSeriesDataFrame
  from autogluon.timeseries.dataset.ts_dataframe import ITEMID, TIMESTAMP
  from autogluon.timeseries.metrics import AVAILABLE_METRICS
  from autogluon.timeseries.models import DeepARModel, ETSModel
  from autogluon.timeseries.models.abstract import AbstractTimeSeriesModel
  from autogluon.timeseries.models.multi_window import MultiWindowBacktestingModel
  from ..common import DUMMY_TS_DATAFRAME, CustomMetric, dict_equal_primitive, get_data_frame_with_item_index
  from .test_gluonts import TESTABLE_MODELS
  from .test_local import TESTABLE_MODELS
  from .test_mlforecast import TESTABLE_MODELS
  from .test_multi_window_model import get_multi_window_deepar
label_desc: Use image node_id 10 for calling a global function with example usage: test_when_fit_called_then_models_train_and_returned_predictor_inference_has_mean_and_correct_quantiles(model_class, quantile_levels, temp_model_path) without return types