code: def forward(
      self,
      x: Tensor,
      y: Tensor,
      index: Optional[Tensor],
      dim_size: Optional[int] = None,
  ) -> Tensor:
      if index is None:
          inp = torch.cat([x, y.expand(x.size(0), -1)], dim=1)
      else:
          inp = torch.cat([x, y[index]], dim=1)
  
      h = inp
      for layer, res in zip(self.layers, self.res_trans):
          h = layer(h)
          h = res(inp) + h
  
      if index is None:
          return h.mean()
  
      if dim_size is None:
          dim_size = int(index.max().item() + 1)
  
      return scatter(h, index, 0, dim_size, reduce="mean").sum()
  
imports: from typing import Callable, List, Optional, Tuple
  import torch
  from torch import Tensor
  from torch_geometric.nn.aggr import Aggregation
  from torch_geometric.nn.inits import reset
  from torch_geometric.utils import scatter
label_desc: Use image node_id 2 for calling the ResNetPotential obj's underlying member method code with example usage: obj.forward(x, y, index, dim_size) and returns: scatter, h