code: def __init__(
      self,
      num_batches_per_epoch: int,
      epochs: int,
      warmstart_epoch_frac: float,
      num_samples_for_loss: int = 200,
      **kwargs,
  ) -> None:
      super().__init__(**kwargs)
      self.warmstart_epoch_frac = warmstart_epoch_frac
      self.epochs = epochs
      self.num_batches_per_epoch = num_batches_per_epoch
      self.batch_no = 0
      self.num_samples_for_loss = num_samples_for_loss
  
imports: from typing import Dict, List, Optional, Tuple
  import mxnet
  import numpy
  from gluonts.core.component import Type, validated
  from gluonts.itertools import prod
  from gluonts.mx.model.deepar import DeepAREstimator
  from gluonts.mx.model.deepar._network import DeepARPredictionNetwork
  from gluonts.mx.model.deepvar_hierarchical._estimator import projection_mat
  from gluonts.mx.model.deepvar_hierarchical._network import coherency_error
  from gluonts.mx.distribution import Distribution, EmpiricalDistribution
  from gluonts.mx import Tensor
  from gluonts.mx.distribution import TransformedPiecewiseLinear
  from gluonts.nursery.temporal_hierarchical_forecasting.utils import utils
  from gluonts.nursery.temporal_hierarchical_forecasting.model.cop_deepar import gluonts_fixes, gnn
label_desc: Use image node_id 1 to create a new COPDeepARTrainingNetwork object from inherited base classes: COPNetwork with example: obj = COPDeepARTrainingNetwork(num_batches_per_epoch, epochs, warmstart_epoch_frac, num_samples_for_loss)