code: def evaluate(num_classes, model, dataloader, desc):
      preds = []
      labels = []
      with torch.no_grad():
          for input_nodes, output_nodes, blocks in tqdm.tqdm(
              dataloader, desc=desc
          ):
              x = blocks[0].srcdata["feat"]
              y = blocks[-1].dstdata["label"]["paper"][:, 0]
              y_hat = model(blocks, x)
              preds.append(y_hat.cpu())
              labels.append(y.cpu())
          preds = torch.cat(preds, 0)
          labels = torch.cat(labels, 0)
          acc = MF.accuracy(
              preds, labels, task="multiclass", num_classes=num_classes
          )
          return acc
  
file_path: dgl/examples/pytorch/rgat/train.py
imports: import dgl
  import dgl.function
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  import tqdm
  from dgl import apply_each
  from dgl.dataloading import DataLoader, NeighborSampler
  from ogb.nodeproppred import DglNodePropPredDataset
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(num_classes, model, dataloader, desc) and returns: acc