code: def __init__(self, num_parallel_samples: int = 100, **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 turtle import distance
  from typing import List, Optional, Tuple, Union
  from matplotlib.pyplot import sca
  import torch
  import torch.nn
  from gluonts.core.component import validated
  from gluonts.torch.modules.distribution_output import DistributionOutput
  from pts.model import weighted_average
  from pts.modules import MeanScaler, NOPScaler, FeatureEmbedder
  from torch.distributions import AffineTransform, Beta, Distribution, Gamma, NegativeBinomial, Normal, Poisson, StudentT, TransformedDistribution
label_desc: Use image node_id 1 to create a new DeepVARPredictionNetwork object from inherited base classes: DeepVARTrainingNetwork with example: obj = DeepVARPredictionNetwork(num_parallel_samples)