code: def forward(
      self, x: Tensor, edge_index: Adj, edge_attr: Tensor
  ) -> Tensor:
      # edge_updater_type: (x: Tensor, edge_attr: Tensor)
      alpha = self.edge_updater(edge_index, x=x, edge_attr=edge_attr)
  
      # propagate_type: (x: Tensor, alpha: Tensor)
      out = self.propagate(edge_index, x=x, alpha=alpha)
      out = out + self.bias
      return out
  
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 3 for calling the GATEConv obj's underlying member method code with example usage: obj.forward(x, edge_index, edge_attr) and returns: out