code: def forward(
      self,
      x: Union[Tensor, OptPairTensor],
      edge_index: Adj,
      edge_weight: OptTensor = None,
      size: Size = None,
  ) -> Tensor:
      if isinstance(x, Tensor):
          x = (x, x)
  
      # propagate_type: (x: OptPairTensor, edge_weight: OptTensor)
      out = self.propagate(
          edge_index, x=x, edge_weight=edge_weight, size=size
      )
      out = self.lin_l(out)
  
      x_r = x[1]
      if x_r is not None:
          out += self.lin_r(x_r)
  
      return out
  
imports: import copy
  from typing import Tuple, Union
  import pytest
  import torch
  from torch import Tensor
  from torch.nn import Linear
  import torch_geometric.typing
  from torch_geometric.nn import MessagePassing, aggr
  from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size, SparseTensor
  from torch_geometric.utils import add_self_loops, scatter, spmm, to_torch_csc_tensor
label_desc: Use image node_id 2 for calling the MyConv obj's underlying member method code with example usage: obj.forward(x, edge_index, edge_weight, size) and returns: out