code: def __init__(self, num_parallel_samples: int, **kwargs) -> None:
      super().__init__(**kwargs)
      self.num_parallel_samples = num_parallel_samples
  
      # for decoding the lags are shifted by one,
      # at the first time-step of the decoder a lag of one corresponds to
      # the last target value
      self.shifted_lags = [l - 1 for l in self.lags_seq]
  
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 1 to create a new DeepVARPredictionNetwork object from inherited base classes: DeepVARNetwork with example: obj = DeepVARPredictionNetwork(num_parallel_samples)