code: def evaluate(
      name,
      g,
      model,
      node_embed,
      device,
      item_set,
      features,
      num_workers,
  ):
      # Switches the model to evaluation mode.
      model.eval()
      category = "paper"
      # An evaluator for the dataset.
      if name == "ogbn-mag":
          evaluator = Evaluator(name=name)
      else:
          evaluator = MAG240MEvaluator()
  
      num_etype = len(g.num_edges)
      data_loader = create_dataloader(
          name,
          g,
          features,
          item_set,
          device,
          batch_size=4096,
          fanouts=[
              torch.full((num_etype,), 25),
              torch.full((num_etype,), 10),
          ],
          shuffle=False,
          num_workers=num_workers,
      )
  
      # To store the predictions.
      y_hats = list()
      y_true = list()
  
      for data in tqdm(data_loader, desc="Inference"):
          # Convert MiniBatch to DGL Blocks and move them to the target device.
          blocks = [block.to(device) for block in data.blocks]
          node_features = extract_node_features(
              name, blocks[0], data, node_embed, device
          )
  
          # Generate predictions.
          logits = model(blocks, node_features)
  
          logits = logits[category]
  
          # Apply softmax to the logits and get the prediction by selecting the
          # argmax.
          y_hat = logits.log_softmax(dim=-1).argmax(
              dim=1, keepdims=True
          )
          y_hats.append(y_hat.cpu())
          y_true.append(data.labels[category].long())
  
      y_pred = torch.cat(y_hats, dim=0)
      y_true = torch.cat(y_true, dim=0)
      y_true = torch.unsqueeze(y_true, 1)
  
      if name == "ogb-lsc-mag240m":
          y_pred = y_pred.view(-1)
          y_true = y_true.view(-1)
  
      return evaluator.eval({"y_true": y_true, "y_pred": y_pred})["acc"]
  
imports: import argparse
  import itertools
  import sys
  import time
  import dgl
  import dgl.graphbolt
  import dgl.nn
  import psutil
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import HeteroEmbedding
  from ogb.lsc import MAG240MEvaluator
  from ogb.nodeproppred import Evaluator
  from tqdm import tqdm
label_desc: Use image node_id 6 for calling a global function with example usage: evaluate(name, g, model, node_embed, device, item_set, features, num_workers) and returns: evaluator