code: def forward(
      self,
      x: Tensor,
      edge_index: Tensor,
      edge_weight: Tensor,
      num_sampled_nodes: Optional[List[int]] = None,
      num_sampled_edges: Optional[List[int]] = None,
  ) -> Tensor:
      for i, conv in enumerate(self.convs):
          if num_sampled_nodes is not None:
              x, edge_index, edge_weight = trim_to_layer(
                  i,
                  num_sampled_nodes,
                  num_sampled_edges,
                  x,
                  edge_index,
                  edge_weight,
              )
          x = conv(x, edge_index, edge_weight)
      return x
  
imports: from typing import List, Optional
  import torch
  from torch import Tensor
  import torch_geometric.typing
  from torch_geometric.data import Data
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import GraphConv
  from torch_geometric.testing import withPackage
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import trim_to_layer
  from torch_geometric.utils._trim_to_layer import trim_sparse_tensor
label_desc: Use image node_id 2 for calling the GNN obj's underlying member method code with example usage: obj.forward(x, edge_index, edge_weight, num_sampled_nodes, num_sampled_edges) and returns: x