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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docstr: Create and return the network used for training (i.e., computing the
  loss).
  
  Returns
  -------
  HybridBlock
      The network that computes the loss given input data.
docstr_tok: ["Create","and","return","the","network","used","for","training","(","i.e.",",","computing","the","loss",")",".","Returns","--","--","--","-","HybridBlock","The","network","that","computes","the","loss","given","input","data","."]
code: def create_training_network(self) -> HybridBlock:
      """
      Create and return the network used for training (i.e., computing the
      loss).
  
      Returns
      -------
      HybridBlock
          The network that computes the loss given input data.
      """
      raise NotImplementedError
  
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 4 for calling the GluonEstimator obj's underlying member method code with example usage: obj.create_training_network() without return types