code: def edge_update(
      self,
      x_j: Tensor,
      x_i: Tensor,
      edge_attr: Tensor,
      index: Tensor,
      ptr: OptTensor,
      size_i: Optional[int],
  ) -> Tensor:
      x_j = F.leaky_relu_(
          self.lin1(torch.cat([x_j, edge_attr], dim=-1))
      )
      alpha_j = (x_j @ self.att_l.t()).squeeze(-1)
      alpha_i = (x_i @ self.att_r.t()).squeeze(-1)
      alpha = alpha_j + alpha_i
      alpha = F.leaky_relu_(alpha)
      alpha = softmax(alpha, index, ptr, size_i)
      alpha = F.dropout(alpha, p=self.dropout, training=self.training)
      return alpha
  
imports: from typing import Optional
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import GRUCell, Linear, Parameter
  from torch_geometric.nn import GATConv, MessagePassing, global_add_pool
  from torch_geometric.nn.inits import glorot, zeros
  from torch_geometric.typing import Adj, OptTensor
  from torch_geometric.utils import softmax
label_desc: Use image node_id 4 for calling the GATEConv obj's underlying member method code with example usage: obj.edge_update(x_j, x_i, edge_attr, index, ptr, size_i) and returns: alpha