code: def evaluate(
      standalone,
      model,
      emb_layer,
      g,
      labels,
      val_nid,
      test_nid,
      batch_size,
      device,
  ):
      """
      Evaluate the model on the validation set specified by ``val_nid``.
      g : The entire graph.
      inputs : The features of all the nodes.
      labels : The labels of all the nodes.
      val_nid : the node Ids for validation.
      batch_size : Number of nodes to compute at the same time.
      device : The GPU device to evaluate on.
      """
      if not standalone:
          model = model.module
      model.eval()
      emb_layer.eval()
      with th.no_grad():
          inputs = load_embs(standalone, emb_layer, g)
          pred = model.inference(g, inputs, batch_size, device)
      model.train()
      emb_layer.train()
      return compute_acc(pred[val_nid], labels[val_nid]), compute_acc(
          pred[test_nid], labels[test_nid]
      )
  
file_path: dgl/examples/pytorch/graphsage/dist/train_dist_transductive.py
imports: import argparse
  import time
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import dgl
  from dgl.distributed import DistEmbedding
  from train_dist import DistSAGE, compute_acc
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(standalone, model, emb_layer, g, labels, val_nid, test_nid, batch_size, device) and returns: compute_acc,  compute_acc