code: def forward(self, x, adj):
      for i, (weight, bias) in enumerate(self.weights):
          x = adj @ x @ weight + bias
          x = np.clip(x, 0, None) if i < len(self.weights) - 1 else x
      return x
  
imports: import argparse
  import torch
  import numpy
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_sparse import SparseTensor
  from torch_geometric.data import GraphSAINTRandomWalkSampler
  from torch_geometric.nn import GCNConv
  from torch_geometric.utils import to_undirected
  from ogb.linkproppred import PygLinkPropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 2 for calling the GCNInference obj's underlying member method code with example usage: obj.forward(x, adj) and returns: x