docstr: Method to enforce domain-specific constraints on the generated samples.
  
  For example, see `DeepVARHierarchicalNetwork`, which adjusts the
  samples so that they satisfy the given aggregation constraints.
  
  For `DeepVAR` this is simply the Identity map.
  
  Parameters
  ----------
  samples
      Tensor of shape (num_parallel_samples*batch_size, 1, target_dim)
  
  Returns
  -------
      Tensor of samples with the same shape.
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code: def post_process_samples(self, samples: Tensor) -> Tensor:
      """
      Method to enforce domain-specific constraints on the generated samples.
  
      For example, see `DeepVARHierarchicalNetwork`, which adjusts the
      samples so that they satisfy the given aggregation constraints.
  
      For `DeepVAR` this is simply the Identity map.
  
      Parameters
      ----------
      samples
          Tensor of shape (num_parallel_samples*batch_size, 1, target_dim)
  
      Returns
      -------
          Tensor of samples with the same shape.
      """
      return samples
  
imports: from typing import List, Optional, Tuple
  import mxnet
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.scaler import MeanScaler, NOPScaler
  from gluonts.mx.distribution import Distribution, DistributionOutput
  from gluonts.mx.util import assert_shape, weighted_average
label_desc: Use image node_id 11 for calling the DeepVARNetwork obj's underlying member method code with example usage: obj.post_process_samples(samples) and returns: samples