code: def forward(self, x: Tensor, adjs_t: List[SparseTensor]) -> Tensor:
      for i, adj_t in enumerate(adjs_t):
          x_target = x[: adj_t.size(0)]
  
          out = self.skips[i](x_target)
          for j in range(self.num_relations):
              edge_type = adj_t.storage.value() == j
              subadj_t = adj_t.masked_select_nnz(
                  edge_type, layout="coo"
              )
              subadj_t = subadj_t.set_value(None, layout=None)
              if subadj_t.nnz() > 0:
                  out += self.convs[i][j]((x, x_target), subadj_t)
  
          x = self.norms[i](out)
          x = F.elu(x) if self.model == "rgat" else F.relu(x)
          x = F.dropout(x, p=self.dropout, training=self.training)
  
      return self.mlp(x)
  
imports: import argparse
  import glob
  import os
  import os.path
  import time
  from typing import List, NamedTuple, Optional
  import numpy
  import torch
  import torch.nn.functional
  from ogb.lsc import MAG240MDataset, MAG240MEvaluator
  import pytorch_lightning
  from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything
  from pytorch_lightning.callbacks import ModelCheckpoint
  from torchmetrics import Accuracy
  from torch import Tensor
  from torch.nn import BatchNorm1d, Dropout, Linear, ModuleList, ReLU, Sequential
  from torch.optim.lr_scheduler import StepLR
  from torch_geometric.data import NeighborSampler
  from torch_geometric.nn import GATConv, SAGEConv
  from torch_sparse import SparseTensor
  from tqdm.auto import tqdm
  from root import ROOT
label_desc: Use image node_id 2 for calling the RGNN obj's underlying member method code with example usage: obj.forward(x, adjs_t) and returns: self