code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      edge_dim: int,
      dropout: float = 0.0,
  ):
      super().__init__(aggr="add", node_dim=0)
  
      self.dropout = dropout
  
      self.att_l = Parameter(torch.empty(1, out_channels))
      self.att_r = Parameter(torch.empty(1, in_channels))
  
      self.lin1 = Linear(in_channels + edge_dim, out_channels, False)
      self.lin2 = Linear(out_channels, out_channels, False)
  
      self.bias = Parameter(torch.empty(out_channels))
  
      self.reset_parameters()
  
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 1 to create a new GATEConv object from inherited base classes: MessagePassing with example: obj = GATEConv(in_channels, out_channels, edge_dim, dropout)