code: def message(self, x2_j, x1_i, x3_j, index, ptr):
      # x1: query, x2: key, x3: value # torch.Size([10556, 64]) ,index torch.Size([10556])
      node, dim = x1_i.size()
      dim_att = dim // self.att_head
      # torch.Size([10556, 8, 8])
      x2_j = x2_j.view(node, self.att_head, dim_att)
      # torch.Size([10556, 8, 8])
      x1_i = x1_i.view(node, self.att_head, dim_att)
      attn = (x2_j * x1_i).sum(dim=-1) / np.sqrt(
          dim_att
      )  # torch.Size([10556, 8])
      attn = softmax(attn, index, ptr)  # torch.Size([10556, 8])
      # torch.Size([10556, 8])
      attn = F.dropout(attn, p=self.dropout, training=self.training)
      out = x3_j.view(node, self.att_head, dim_att) * attn.unsqueeze(-1)
      out = out.view(-1, dim)
      return out
  
imports: from torch_geometric.nn import MessagePassing
  from torch_geometric.utils import softmax
  import torch
  import torch.nn
  import torch.nn.functional
  from torch_geometric.nn import GCNConv, SAGEConv, GATConv, ARMAConv, ChebConv, GatedGraphConv, SGConv
  from typing import Union, Tuple, Optional
  from torch_geometric.typing import OptPairTensor, Adj, Size, NoneType, OptTensor
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch.nn import Parameter, Linear
  from torch_sparse import SparseTensor, set_diag
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.utils import remove_self_loops, add_self_loops, softmax
  from torch_geometric.nn.inits import glorot, zeros
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.functional
  from torch.nn import Parameter
  from torch_geometric.nn.inits import glorot, zeros
  from torch_geometric.utils import softmax
  from torch_scatter import scatter_add
  import numpy
  from ..graph_nas_macro import GeoLayer
label_desc: Use image node_id 4 for calling the AggAttn obj's underlying member method code with example usage: obj.message(x2_j, x1_i, x3_j, index, ptr) and returns: out