code: def __init__(
      self,
      in_feats,
      out_feats,
      num_heads,
      ffn_drop=0.0,
      attn_drop=0.0,
      trans=True,
  ):
      super(GAT, self).__init__()
      self._num_heads = num_heads
      self._in_feats = in_feats
      self._out_feats = out_feats
      self.q_proj = nn.Linear(
          in_feats, num_heads * out_feats, bias=False
      )
      self.k_proj = nn.Linear(
          in_feats, num_heads * out_feats, bias=False
      )
      self.v_proj = nn.Linear(
          in_feats, num_heads * out_feats, bias=False
      )
      self.attn_drop = nn.Dropout(0.1)
      self.ln1 = nn.LayerNorm(in_feats)
      self.ln2 = nn.LayerNorm(in_feats)
      if trans:
          self.FFN = nn.Sequential(
              nn.Linear(in_feats, 4 * in_feats),
              nn.PReLU(4 * in_feats),
              nn.Linear(4 * in_feats, in_feats),
              nn.Dropout(0.1),
          )
          # a strange FFN, see the author's code
      self._trans = trans
  
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 1 to create a new GAT object from inherited base classes: nn with example: obj = GAT(in_feats, out_feats, num_heads, ffn_drop, attn_drop, trans)