code: def forward_layer(self, layer, x, adj_t, state):
      # We keep the skip connections in GPU memory for now. If one encounters
      # GPU memory problems, it is advised to push `state['xs']` to the CPU.
      if layer == 0:
          if self.drop_input:
              x = F.dropout(x, p=self.dropout, training=self.training)
  
          x = self.lins[0](x)
          state["xs"] = [x[: adj_t.size(0)]]
  
      h = self.convs[layer](x, adj_t)
      if self.batch_norm:
          h = self.bns[layer](h)
      if self.residual:
          h += x[: h.size(0)]
      h = h.relu_()
      state["xs"] += [h]
      h = F.dropout(h, p=self.dropout, training=self.training)
  
      if layer == self.num_layers - 1:
          h = torch.cat(state["xs"], dim=-1)
          h = F.dropout(h, p=self.dropout, training=self.training)
          h = self.lins[1](h)
  
      return h
  
file_path: DIG/dig/lsgraph/method/GraphFMOB/models/pna_jk.py
imports: from typing import Optional, List
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch.nn import ModuleList, Linear, BatchNorm1d, Sequential, ReLU, Identity
  from torch_sparse import SparseTensor
  from ..models import ScalableGNN
  from ..models.pna import PNAConv
label_desc: Use image node_id 6 for calling the PNA_JK obj's underlying member method code with example usage: obj.forward_layer(layer, x, adj_t, state) and returns: h