code: def predict(
      self,
      dataset: Iterable[Dict],
      batch_size: Optional[int] = None,
      **kwargs,
  ) -> Iterator[SampleForecast]:
      if batch_size is None:
          batch_size = self.batch_size
      assert batch_size > 0
      if batch_size is None or batch_size == 1:
          return self._predict_serial(dataset, **kwargs)
      else:
          return self._predict_batch(
              dataset, batch_size=batch_size, **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 8 for calling the TabularPredictor obj's underlying member method code with example usage: obj.predict(dataset, batch_size) and returns: self, self