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 from_hyperparameters(cls, **hyperparameters):
      return from_hyperparameters(cls, **hyperparameters)
  
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 5 for calling the Predictor obj's underlying member method code with example usage: obj.from_hyperparameters(cls) and returns: from_hyperparameters