code: def message(
      self,
      x_j: Tensor,
      alpha_j: Tensor,
      alpha_i: OptTensor,
      index: Tensor,
      ptr: OptTensor,
  ) -> Tensor:
      alpha = alpha_j if alpha_i is None else alpha_j + alpha_i
      alpha = F.leaky_relu(alpha, self.negative_slope)
      alpha = softmax(alpha, index, ptr)
      alpha = F.dropout(alpha, p=self.dropout, training=self.training)
      return x_j * alpha.unsqueeze(-1)
  
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 GATConv2 obj's underlying member method code with example usage: obj.message(x_j, alpha_j, alpha_i, index, ptr) and returns: unknown