code: def forward_layer(self, layer, x, adj_t, state):
      if layer == 0:
          if self.drop_input:
              x = F.dropout(x, p=self.dropout, training=self.training)
  
          x = x_0 = self.lins[0](x).relu_()
          state["x_0"] = x_0
  
      x = F.dropout(x, p=self.dropout, training=self.training)
      h = self.convs[layer](x, state["x_0"], adj_t)
      if self.batch_norm:
          h = self.bns[layer](h)
      if self.residual and h.size(-1) == x.size(-1):
          h += x[: h.size(0)]
      x = h.relu_()
  
      if layer == self.num_layers - 1:
          x = F.dropout(x, p=self.dropout, training=self.training)
          x = self.lins[1](x)
  
      return x
  
imports: from typing import Optional
  from torch_geometric.typing import Adj, OptTensor
  import torch.nn.functional
  from torch.nn import ModuleList, Linear, BatchNorm1d
  from torch_geometric.nn import GCN2Conv
  import torch
  from torch import Tensor
  from torch_sparse import SparseTensor
  from ..models import ScalableGNN
label_desc: Use image node_id 6 for calling the GCN2 obj's underlying member method code with example usage: obj.forward_layer(layer, x, adj_t, state) and returns: x