code: def forward(self, graph, feat):
      graph = graph.local_var()
      feat_c = feat.clone().detach().requires_grad_(False)
      q, k, v = (
          self.q_proj(feat),
          self.k_proj(feat_c),
          self.v_proj(feat_c),
      )
      q = q.view(-1, self._num_heads, self._out_feats)
      k = k.view(-1, self._num_heads, self._out_feats)
      v = v.view(-1, self._num_heads, self._out_feats)
      graph.ndata.update(
          {"ft": v, "el": k, "er": q}
      )  # k,q instead of q,k, the edge_softmax is applied on incoming edges
      # compute edge attention
      graph.apply_edges(fn.u_dot_v("el", "er", "e"))
      e = graph.edata.pop("e") / math.sqrt(
          self._out_feats * self._num_heads
      )
      graph.edata["a"] = edge_softmax(graph, e)
      # message passing
      graph.update_all(fn.u_mul_e("ft", "a", "m"), fn.sum("m", "ft2"))
      rst = graph.ndata["ft2"]
      # residual
      rst = rst.view(feat.shape) + feat
      if self._trans:
          rst = self.ln1(rst)
          rst = self.ln1(rst + self.FFN(rst))
          # use the same layer norm, see the author's code
      return rst
  
imports: import math
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from utlis import *
  import dgl.function
  from dgl.nn.functional import edge_softmax
label_desc: Use image node_id 2 for calling the GAT obj's underlying member method code with example usage: obj.forward(graph, feat) and returns: rst