class_docstr: The training configuration may be derived by any estimator which is trained
  via the GluonTS `Trainer` class.
docstr: Creates a predictor from the provided network. This method is required
  to be implemented by every model configuration which describes a
  trainable model.
  
  Args:
      estimator: The estimator for which to obtain the predictor. Must have been obtained
      from the same model configuration. Otherwise, behavior is undefined.
      network: The network which has been trained.
  
  Returns:
      The predictor created from the network.
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code: def create_predictor(
      self, estimator: Estimator, network: nn.HybridBlock
  ) -> Predictor:
      """
      Creates a predictor from the provided network. This method is required
      to be implemented by every model configuration which describes a
      trainable model.
  
      Args:
          estimator: The estimator for which to obtain the predictor. Must have been obtained
          from the same model configuration. Otherwise, behavior is undefined.
          network: The network which has been trained.
  
      Returns:
          The predictor created from the network.
      """
      raise NotImplementedError
  
imports: from dataclasses import asdict, dataclass, MISSING
  from pathlib import Path
  from typing import Any, Dict, List, Optional, TypeVar
  from gluonts.model.estimator import Estimator
  from gluonts.model.predictor import Predictor
  from mxnet.gluon import nn
  from tsbench.config.dataset import DatasetConfig
  from tsbench.gluonts import TimedTrainer
  from tsbench.gluonts.callbacks import Callback
label_desc: Use image node_id 2 for calling the TrainConfig obj's underlying member method code with example usage: obj.create_predictor(estimator, network) without return types