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 typing import List, Optional, Tuple
  import mxnet
  from gluonts.core.component import validated
  from gluonts.itertools import prod
  from gluonts.mx import Tensor
  from gluonts.mx.block.feature import FeatureEmbedder
  from gluonts.mx.block.scaler import MeanScaler, NOPScaler
  from gluonts.mx.distribution import DistributionOutput
  from gluonts.mx.util import weighted_average
  from .trans_decoder import TransformerDecoder
  from .trans_encoder import TransformerEncoder
label_desc: Use image node_id 1 to create a new TransformerPredictionNetwork object from inherited base classes: TransformerNetwork with example: obj = TransformerPredictionNetwork(num_parallel_samples)