class_docstr: An `Estimator` type with utilities for creating PyTorch-Lightning-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
  -------
  pl.LightningModule
      The network that computes the loss given input data.
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code: def create_lightning_module(self) -> pl.LightningModule:
      """
      Create and return the network used for training (i.e., computing the
      loss).
  
      Returns
      -------
      pl.LightningModule
          The network that computes the loss given input data.
      """
      raise NotImplementedError
  
imports: from typing import NamedTuple, Optional, Iterable, Dict, Any
  import logging
  import numpy
  import lightning.pytorch
  import torch.nn
  from gluonts.core.component import validated
  from gluonts.dataset.common import Dataset
  from gluonts.env import env
  from gluonts.itertools import Cached
  from gluonts.model import Estimator, Predictor
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.transform import Transformation
label_desc: Use image node_id 3 for calling the PyTorchLightningEstimator obj's underlying member method code with example usage: obj.create_lightning_module() without return types