code: def __init__(
      self,
      ag_model,
      prediction_length: int,
      time_features: List[TimeFeature],
      lag_indices: List[int],
      scaling: Callable[[pd.Series], Tuple[pd.Series, float]],
      batch_size: Optional[int] = 32,
      quantiles_to_predict: Optional[List[float]] = None,
      dtype=np.float32,
  ) -> None:
      super().__init__(prediction_length=prediction_length)
      assert all(lag_idx >= 1 for lag_idx in lag_indices)
  
      self.ag_model = ag_model
      self.time_features = time_features
      self.lag_indices = lag_indices
      self.scaling = scaling
      self.batch_size = batch_size
      self.dtype = dtype
      self.quantiles_to_predict = quantiles_to_predict
      self.forecast_keys = (
          [str(q) for q in quantiles_to_predict]
          if quantiles_to_predict is not None
          else None
      )
  
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 1 to create a new TabularPredictor object from inherited base classes: Predictor with example: obj = TabularPredictor(ag_model, prediction_length, time_features, lag_indices, scaling, batch_size, quantiles_to_predict, dtype)