class_docstr: Abstract class representing predictor objects.
  Parameters
  ----------
  prediction_length
      Prediction horizon.
class_docstr_tok: ["Abstract","class","representing","predictor","objects",".","Parameters","--","--","--","--","--","prediction_length","Prediction","horizon","."]
code: def __init__(
      self, prediction_length: int, lead_time: int = 0
  ) -> None:
      assert (
          prediction_length > 0
      ), "The value of `prediction_length` should be > 0"
      assert lead_time >= 0, "The value of `lead_time` should be >= 0"
  
      self.prediction_length = prediction_length
      self.lead_time = lead_time
  
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 to create a new Predictor object  with example: obj = Predictor(prediction_length, lead_time)