code: def evaluate(
      model, g, inputs, labels, val_nid, test_nid, batch_size, device
  ):
      """
      Evaluate the model on the validation and test set.
  
      Parameters
      ----------
      model : DistSAGE
          The model to be evaluated.
      g : DistGraph
          The entire graph.
      inputs : DistTensor
          The feature data of all the nodes.
      labels : DistTensor
          The labels of all the nodes.
      val_nid : torch.Tensor
          The node IDs for validation.
      test_nid : torch.Tensor
          The node IDs for test.
      batch_size : int
          Batch size for evaluation.
      device : torch.Device
          The target device to evaluate on.
  
      Returns
      -------
      float
          Validation accuracy.
      float
          Test accuracy.
      """
      model.eval()
      with th.no_grad():
          pred = model.inference(g, inputs, batch_size, device)
      model.train()
      return compute_acc(pred[val_nid], labels[val_nid]), compute_acc(
          pred[test_nid], labels[test_nid]
      )
  
file_path: dgl/examples/distributed/graphsage/node_classification.py
imports: import argparse
  import socket
  import time
  import dgl
  import dgl.nn.pytorch
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import tqdm
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, g, inputs, labels, val_nid, test_nid, batch_size, device) and returns: compute_acc,  compute_acc