class_docstr: An abstract predictor that can be subclassed by framework-specific models.
  Subclasses should have ``@validated()`` constructors:
  (de)serialization and equality test are all implemented on top of its logic.
  
  Parameters
  ----------
  prediction_length
      Prediction horizon.
  lead_time
      Prediction lead time.
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code: def predict(self, dataset: Dataset, **kwargs) -> Iterator[Forecast]:
      for item in dataset:
          yield self.predict_item(item)
  
imports: import itertools
  import json
  import logging
  import multiprocessing
  import sys
  import traceback
  from pathlib import Path
  from pydoc import locate
  from tempfile import TemporaryDirectory
  from typing import TYPE_CHECKING, Callable, Iterator, Optional, Dict, Any
  import numpy
  import gluonts
  from gluonts.core import fqname_for
  from gluonts.core.component import equals, from_hyperparameters
  from gluonts.core.serde import dump_json, load_json
  from gluonts.dataset.common import DataEntry, Dataset
  from gluonts.model.forecast import Forecast
label_desc: Use image node_id 1 for calling the RepresentablePredictor obj's underlying member method code with example usage: obj.predict(dataset) without return types