code: def forward(self, data):
      x, edge_index, batch = data.x, data.edge_index, data.batch
      if "edge_weights" in data:
          edge_weight = data.edge_weights.float()
      else:
          edge_weight = None
      x = self.conv1(x, edge_index, edge_weight=edge_weight)
      for conv in self.convs:
          x = conv(x, edge_index, edge_weight=edge_weight)
  
      h = self.pool(x, batch)
  
      x = F.relu(self.lin1(h))
      x = F.dropout(x, p=self.dropout, training=self.training)
      x = self.lin2(x)
      # return F.log_softmax(x, dim=-1)
      return h, x
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch_geometric.transforms
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.glob import global_add_pool, global_mean_pool, global_max_pool
  from typing import List, Optional, Union, Callable
  from torch import Tensor
  from torch_scatter import scatter
  from torch_geometric.nn.models.basic_gnn import BasicGNN
  from torch_geometric.nn.models import MLP
  from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size
  from torch_sparse import SparseTensor, matmul
  from torch.nn import Linear, Sequential, ReLU, BatchNorm1d
  from torch_geometric.nn.inits import reset
label_desc: Use image node_id 3 for calling the GIN obj's underlying member method code with example usage: obj.forward(data) and returns: h,  x