code: def hybrid_forward(
      self, F, x: Tensor, static: Tensor, mask: Tensor
  ) -> Tensor:
      static = F.tile(
          static,
          reps=(1, self.context_length + self.prediction_length, 1),
      )
      skip = F.slice_axis(
          x, axis=1, begin=self.context_length, end=None
      )
      x = self.enrich(x, static)
      mask_pad = F.slice_axis(F.ones_like(mask), axis=1, begin=0, end=1)
      mask_pad = F.tile(mask_pad, reps=(1, self.prediction_length))
      mask = F.concat(mask, mask_pad, dim=1)
      att = self.attention(x, mask)
      att = self.att_net(att)
      x = F.slice_axis(x, axis=1, begin=self.context_length, end=None)
      x = self.att_lnorm(F.broadcast_add(x, att))
      x = self.ff_net(x)
      x = self.ff_lnorm(F.broadcast_add(x, skip))
      return x
  
imports: import math
  from typing import List, Optional, Tuple
  from mxnet import init
  from mxnet.gluon import HybridBlock, nn, rnn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 2 for calling the TemporalFusionDecoder obj's underlying member method code with example usage: obj.hybrid_forward(F, x, static, mask) and returns: x