code: def _postprocess(
      self,
      preds: torch.Tensor,
      offset: torch.Tensor,
      scale: torch.Tensor,
  ) -> torch.Tensor:
      offset = offset.unsqueeze(-1)
      scale = scale.unsqueeze(-1)
      preds = (preds * (scale + self.normalize_eps)) + offset
      return preds
  
imports: from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  from torch.distributions import Distribution
  from gluonts.core.component import validated
  from pts.model import weighted_average
  from .tft_modules import FeatureProjector, FeatureEmbedder, VariableSelectionNetwork, GatedResidualNetwork, TemporalFusionEncoder, TemporalFusionDecoder
  from .tft_output import QuantileOutput
label_desc: Use image node_id 3 for calling the TemporalFusionTransformerNetwork obj's underlying member method code with example usage: obj._postprocess(preds, offset, scale) and returns: preds