code: def __init__(
      self,
      num_samples_for_loss: int,
      likelihood_weight: float,
      CRPS_weight: float,
      coherent_train_samples: bool,
      warmstart_epoch_frac: float,
      epochs: float,
      num_batches_per_epoch: float,
      sample_LH: bool,
      **kwargs,
  ) -> None:
      super().__init__(**kwargs)
      self.num_samples_for_loss = num_samples_for_loss
      self.likelihood_weight = likelihood_weight
      self.CRPS_weight = CRPS_weight
      self.coherent_train_samples = coherent_train_samples
      self.warmstart_epoch_frac = warmstart_epoch_frac
      self.epochs = epochs
      self.num_batches_per_epoch = num_batches_per_epoch
      self.batch_no = 0
      self.sample_LH = sample_LH
  
      # Assert CRPS_weight, likelihood_weight, and coherent_train_samples
      # have harmonious values
      assert self.CRPS_weight >= 0.0, "CRPS weight must be non-negative"
      assert (
          self.likelihood_weight >= 0.0
      ), "Likelihood weight must be non-negative!"
      assert (
          self.likelihood_weight + self.CRPS_weight > 0.0
      ), "At least one of CRPS or likelihood weights must be non-zero"
      if self.CRPS_weight == 0.0 and self.coherent_train_samples:
          assert (
              "No sampling being performed. "
              "coherent_train_samples flag is ignored"
          )
      if not self.sample_LH == 0.0 and self.coherent_train_samples:
          assert (
              "No sampling being performed. "
              "coherent_train_samples flag is ignored"
          )
      if self.likelihood_weight == 0.0 and self.sample_LH:
          assert (
              "likelihood_weight is 0 but sample likelihoods are still "
              "being calculated. Set sample_LH=0 when likelihood_weight=0"
          )
  
imports: import logging
  from typing import List, Optional
  from itertools import product
  import mxnet
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.distribution import Distribution, DistributionOutput
  from gluonts.mx.distribution import EmpiricalDistribution
  from gluonts.mx.util import assert_shape
  from gluonts.mx.distribution import LowrankMultivariateGaussian
  from gluonts.mx.model.deepvar._network import DeepVARNetwork, DeepVARTrainingNetwork, DeepVARPredictionNetwork
label_desc: Use image node_id 1 to create a new DeepVARHierarchicalTrainingNetwork object from inherited base classes: DeepVARHierarchicalNetwork, DeepVARTrainingNetwork with example: obj = DeepVARHierarchicalTrainingNetwork(num_samples_for_loss, likelihood_weight, CRPS_weight, coherent_train_samples, warmstart_epoch_frac, epochs, num_batches_per_epoch, sample_LH)