code: def evaluate_in_batches(dataloader, device, model):
      total_score = 0
      for batch_id, batched_graph in enumerate(dataloader):
          batched_graph = batched_graph.to(device)
          features = batched_graph.ndata["feat"]
          labels = batched_graph.ndata["label"]
          score = evaluate(batched_graph, features, labels, model)
          total_score += score
      return total_score / (batch_id + 1)
  
file_path: dgl/examples/pytorch/gat/train_ppi.py
imports: import dgl.nn
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.data.ppi import PPIDataset
  from dgl.dataloading import GraphDataLoader
  from sklearn.metrics import f1_score
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate_in_batches(dataloader, device, model) and returns: unknown