code: def forward(self, h, h_cache, key_pe):
      # h = B x M x H
      # h_cache = B x L x H
      h_all = torch.cat([h_cache, h], dim=1)  # B x (M+L) x H
      attn_out = self.attn(h, h_all, h_all, key_pe)
      h = self.norm1(h + attn_out)  # B x M x H
      if self.ff is not None:
          ff_out = self.ff(h)
          out = self.norm2(h + ff_out)  # B x M x H
      else:
          out = h
      return out
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq.modules.layer_norm import LayerNorm
  from .adaptive_span_attention import AdaptiveSpan
label_desc: Use image node_id 2 for calling the TransformerSeqLayer obj's underlying member method code with example usage: obj.forward(h, h_cache, key_pe) and returns: out