code: def evaluate(proc_id, model, g, device, use_uva):
      model.eval()
      batch_size = 10000
      with torch.no_grad():
          pred = model.module.inference(g, device, batch_size, use_uva)
      return pred
  
imports: import argparse
  import os
  import time
  import dgl.function
  import dgl.nn
  import numpy
  import sklearn.linear_model
  import sklearn.metrics
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.data import AsNodePredDataset, RedditDataset
  from dgl.dataloading import as_edge_prediction_sampler, DataLoader, MultiLayerFullNeighborSampler, NeighborSampler
  from dgl.multiprocessing import shared_tensor
  from ogb.nodeproppred import DglNodePropPredDataset
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(proc_id, model, g, device, use_uva) and returns: pred