class_docstr: An `Estimator` type with utilities for creating Gluon-based models.
  
  To extend this class, one needs to implement three methods:
  `create_transformation`, `create_training_network`, `create_predictor`,
  `create_training_data_loader`, and `create_validation_data_loader`.
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code: def from_hyperparameters(cls, **hyperparameters) -> "GluonEstimator":
      Model = getattr(cls.__init__, "Model", None)
  
      if not Model:
          raise AttributeError(
              "Cannot find attribute Model attached to the "
              f"{fqname_for(cls)}. Most probably you have forgotten to mark "
              "the class constructor as @validated()."
          )
  
      try:
          trainer = from_hyperparameters(Trainer, **hyperparameters)
  
          return cls(
              **Model(
                  **{**hyperparameters, "trainer": trainer}
              ).__dict__
          )
      except ValidationError as e:
          raise GluonTSHyperparametersError from e
  
imports: from typing import NamedTuple, Optional, Type, Union
  import numpy
  from gluonts.core import fqname_for
  from gluonts.core.component import GluonTSHyperparametersError, from_hyperparameters, validated
  from gluonts.dataset.common import Dataset
  from gluonts.dataset.loader import DataLoader
  from gluonts.env import env
  from gluonts.itertools import Cached
  from gluonts.model import Estimator, Predictor
  from gluonts.mx.model.predictor import GluonPredictor
  from gluonts.mx.trainer import Trainer
  from gluonts.mx.util import copy_parameters
  from gluonts.pydantic import ValidationError
  from gluonts.transform import Transformation, TransformedDataset
  from mxnet.gluon import HybridBlock
label_desc: Use image node_id 2 for calling the GluonEstimator obj's underlying member method code with example usage: obj.from_hyperparameters(cls) and returns: cls