code: def _predict_batch(
      self, dataset: Iterable[Dict], batch_size: int, **kwargs
  ) -> Iterator[SampleForecast]:
      for batch in batcher(dataset, batch_size):
          yield from (
              self._predict_batch_autoreg(batch, **kwargs)
              if self.auto_regression
              else self._predict_batch_one_shot(batch, **kwargs)
          )
  
imports: from typing import Callable, Dict, List, Optional, Iterator, Iterable, Tuple
  import numpy
  import pandas
  import shutil
  from pathlib import Path
  from autogluon.tabular import TabularPredictor
  from gluonts.core.serde import dump_json, load_json
  from gluonts.dataset.util import to_pandas
  from gluonts.dataset.field_names import FieldName
  from gluonts.itertools import batcher
  from gluonts.model.forecast import SampleForecast, QuantileForecast, Forecast
  from gluonts.model.predictor import Predictor
  from gluonts.time_feature import TimeFeature
label_desc: Use image node_id 7 for calling the TabularPredictor obj's underlying member method code with example usage: obj._predict_batch(dataset, batch_size) without return types