code: def forward(
      self, w, r_emb, r_w_bias, r_bias, attn_mask=None, mems=None
  ):
      # r_emb: [klen, n_head, d_head], used for term B
      # r_w_bias: [n_head, d_head], used for term C
      # r_bias: [klen, n_head], used for term D
  
      qlen, bsz = w.size(0), w.size(1)
  
      if mems is not None:
          cat = torch.cat([mems, w], 0)
          if self.pre_lnorm:
              w_heads = self.qkv_net(self.layer_norm(cat))
          else:
              w_heads = self.qkv_net(cat)
          w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
  
          w_head_q = w_head_q[-qlen:]
      else:
          if self.pre_lnorm:
              w_heads = self.qkv_net(self.layer_norm(w))
          else:
              w_heads = self.qkv_net(w)
          w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
  
      klen = w_head_k.size(0)
  
      w_head_q = w_head_q.view(qlen, bsz, self.n_head, self.d_head)
      w_head_k = w_head_k.view(klen, bsz, self.n_head, self.d_head)
      w_head_v = w_head_v.view(klen, bsz, self.n_head, self.d_head)
  
      if klen > r_emb.size(0):
          r_emb_pad = r_emb[0:1].expand(klen - r_emb.size(0), -1, -1)
          r_emb = torch.cat([r_emb_pad, r_emb], 0)
          r_bias_pad = r_bias[0:1].expand(klen - r_bias.size(0), -1)
          r_bias = torch.cat([r_bias_pad, r_bias], 0)
      else:
          r_emb = r_emb[-klen:]
          r_bias = r_bias[-klen:]
  
      #### compute attention score
      rw_head_q = (
          w_head_q + r_w_bias[None]
      )  # qlen x bsz x n_head x d_head
  
      AC = torch.einsum(
          "ibnd,jbnd->ijbn", (rw_head_q, w_head_k)
      )  # qlen x klen x bsz x n_head
      B_ = torch.einsum(
          "ibnd,jnd->ijbn", (w_head_q, r_emb)
      )  # qlen x klen x bsz x n_head
      D_ = r_bias[None, :, None]  # 1    x klen x 1   x n_head
      BD = self._rel_shift(B_ + D_)
  
      # [qlen x klen x bsz x n_head]
      attn_score = AC + BD
      attn_score.mul_(self.scale)
  
      #### compute attention probability
      if attn_mask is not None and attn_mask.any().item():
          if attn_mask.dim() == 2:
              attn_score.masked_fill_(
                  attn_mask[None, :, :, None].bool(), -float("inf")
              )
          elif attn_mask.dim() == 3:
              attn_score.masked_fill_(
                  attn_mask[:, :, :, None].bool(), -float("inf")
              )
  
      # [qlen x klen x bsz x n_head]
      attn_prob = F.softmax(attn_score, dim=1)
      attn_prob = self.dropatt(attn_prob)
  
      #### compute attention vector
      attn_vec = torch.einsum("ijbn,jbnd->ibnd", (attn_prob, w_head_v))
  
      # [qlen x bsz x n_head x d_head]
      attn_vec = attn_vec.contiguous().view(
          attn_vec.size(0), attn_vec.size(1), self.n_head * self.d_head
      )
  
      ##### linear projection
      attn_out = self.o_net(attn_vec)
      attn_out = self.drop(attn_out)
  
      if self.pre_lnorm:
          ##### residual connection
          output = w + attn_out
      else:
          ##### residual connection + layer normalization
          output = self.layer_norm(w + attn_out)
  
      return output
  
imports: import sys
  import math
  import functools
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from proj_adaptive_softmax import ProjectedAdaptiveLogSoftmax, Projection
  from log_uniform_sampler import LogUniformSampler, sample_logits
  from fmoe import FMoETransformerMLP
label_desc: Use image node_id 2 for calling the RelLearnableMultiHeadAttn obj's underlying member method code with example usage: obj.forward(w, r_emb, r_w_bias, r_bias, attn_mask, mems) and returns: output