code: def forward(
      self, x: torch.Tensor, static: torch.Tensor, mask: torch.Tensor
  ) -> torch.Tensor:
      static = static.repeat(
          (1, self.context_length + self.prediction_length, 1)
      )
  
      skip = x[:, self.context_length :, ...]
      x = self.enrich(x, static)
  
      mask_pad = torch.ones_like(mask)[:, 0:1, ...]
      mask_pad = mask_pad.repeat((1, self.prediction_length))
      key_padding_mask = torch.cat((mask, mask_pad), dim=1).bool()
  
      query_key_value = x.permute(1, 0, 2)
  
      attn_output, _ = self.attention(
          query=query_key_value[-self.prediction_length :, ...],
          key=query_key_value,
          value=query_key_value,
          # key_padding_mask=key_padding_mask, # does not work on GPU :-(
          attn_mask=self.attn_mask,
      )
      att = self.att_net(attn_output.permute(1, 0, 2))
  
      x = x[:, self.context_length :, ...]
      x = self.att_lnorm(x + att)
      x = self.ff_net(x)
      x = self.ff_lnorm(x + skip)
  
      return x
  
imports: from typing import List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  from pts.modules import FeatureEmbedder
label_desc: Use image node_id 3 for calling the TemporalFusionDecoder obj's underlying member method code with example usage: obj.forward(x, static, mask) and returns: x