code: def propagate(self, graph, feat):
      # x_i torch.Size([13264, 2, 4])
      # x_j torch.Size([13264, 2, 4])
      # edge_index torch.Size([2, 13264])
      # num_nodes 2708
      # x_i is target; x_j is source
      edge_index = torch.stack(graph.edges())
      x_i = torch.index_select(feat, 0, edge_index[1])
      x_j = torch.index_select(feat, 0, edge_index[0])
      num_nodes = graph.num_nodes()
      if self.att_type == "const":  # Const OK
          if self.training and self.dropout > 0:
              x_j = F.dropout(x_j, p=self.dropout, training=True)
          neighbor = x_j
      elif self.att_type == "gcn":  # GCN OK
          if self.gcn_weight is None or self.gcn_weight.size(
              0
          ) != x_j.size(
              0
          ):  # calculate norm by degree like GCN
              _, norm = self.norm(edge_index, num_nodes, None)
              self.gcn_weight = norm
          neighbor = self.gcn_weight.view(-1, 1, 1) * x_j
      else:  # Attention OK
          # Compute attention coefficients.
          alpha = self.apply_attention(edge_index, num_nodes, x_i, x_j)
          alpha = softmax(alpha, edge_index[0], num_nodes=num_nodes)
          # Sample attention coefficients stochastically.
          if self.training and self.dropout > 0:
              alpha = F.dropout(alpha, p=self.dropout, training=True)
  
          neighbor = x_j * alpha.view(-1, self.heads, 1)
  
      if self.pool_dim > 0:
          # neighbor torch.Size([13264, 2, 4])
          for layer in self.pool_layer:
              neighbor = layer(neighbor)
  
      graph.edata["e"] = neighbor
      # aggregate : need neighbor as edge, ok
      if self.reduce_func is not None:
          graph.update_all(
              fn.copy_e("e", "e"), self.reduce_func("e", "h")
          )
          out = graph.ndata["h"]
          graph.edata.pop("e")
      else:
          out = neighbor
          pass
  
      out = self.update(out)
      return out
  
imports: import torch
  from torch import nn
  import torch.nn.functional
  import dgl
  from dgl.nn.pytorch.conv import *
  import dgl.function
  import math
  from dgl.utils import expand_as_pair
  from dgl.nn.functional import edge_softmax
  from torch.nn import Parameter
  import torch
  from torch import Tensor
  from ..scatter_utils import *
  from typing import Optional, Tuple, Union
label_desc: Use image node_id 5 for calling the GeoLayer obj's underlying member method code with example usage: obj.propagate(graph, feat) and returns: out