code: def _postprocess(
      self,
      F,
      preds: Tensor,
      offset: Tensor,
      scale: Tensor,
  ) -> Tensor:
      offset = F.expand_dims(offset, axis=-1)
      scale = F.expand_dims(scale, axis=-1)
      preds = F.broadcast_add(
          F.broadcast_mul(preds, (scale + self.normalize_eps)),
          offset,
      )
      return preds
  
imports: from typing import List, Tuple, Type
  import numpy
  from mxnet.gluon import HybridBlock, nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.feature import FeatureEmbedder
  from gluonts.mx.block.quantile_output import QuantileOutput
  from gluonts.mx.util import weighted_average
  from ._layers import GatedResidualNetwork, TemporalFusionDecoder, TemporalFusionEncoder, VariableSelectionNetwork
label_desc: Use image node_id 3 for calling the TemporalFusionTransformerNetwork obj's underlying member method code with example usage: obj._postprocess(F, preds, offset, scale) and returns: preds