code: def forward(self, x: Tensor, adj_t: SparseTensor, *args) -> Tensor:
      if len(args) > 0:
          batch_size = args[0]
          n_id = args[1]
          P_bar = adj_t[:, batch_size:]
          P_tilde = P_bar.t()
      else:
          print("no batch_size in the forward of conv")
  
      if self.drop_input:
          x = F.dropout(x, p=self.dropout, training=self.training)
  
      if self.linear:
          x = self.lins[0](x).relu_()
          x = F.dropout(x, p=self.dropout, training=self.training)
  
      for layer_idx, (conv, bn, hist) in enumerate(
          zip(self.convs[:-1], self.bns, self.histories)
      ):
          x_bar_out_batch = P_tilde.spmm(x[:batch_size]).detach()
  
          h = conv(x, adj_t)
          h = bn(h)
          if self.residual and h.size(-1) == x.size(-1):
              h += x[: h.size(0)]
          x = h.relu_()
  
          if n_id is not None and batch_size is not None:
              with torch.no_grad():
                  if self.batch_norm:
                      bn.eval()
                      x_bar_out_batch = bn(
                          conv.transformation(x_bar_out_batch)
                      ).relu()
                      hist.forward(x_bar_out_batch, n_id[batch_size:])
                      bn.train()
                  else:
                      x_bar_out_batch = conv.transformation(
                          x_bar_out_batch
                      ).relu()
                      hist.forward(x_bar_out_batch, n_id[batch_size:])
  
          x = self.push_and_pull(hist, x, *args)
          x = F.dropout(x, p=self.dropout, training=self.training)
  
      h = self.convs[-1](x, adj_t)
  
      if not self.linear:
          return h
  
      if self.batch_norm:
          h = self.bns[-1](h)
  
      if self.residual and h.size(-1) == x.size(-1):
          h += x[: h.size(0)]
      h = h.relu_()
      h = F.dropout(h, p=self.dropout, training=self.training)
      return self.lins[1](h)
  
imports: from typing import Optional
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch.nn import ModuleList, Linear, BatchNorm1d
  from torch_sparse import SparseTensor
  from torch_geometric.nn import GCNConv
  from ..models import ScalableGNN
  from torch_geometric.typing import Adj, OptTensor
label_desc: Use image node_id 5 for calling the GCN obj's underlying member method code with example usage: obj.forward(x, adj_t) and returns: self, h