code: def collate(samples):
      # The input `samples` is a list of pairs
      #  (graph, label).
      graphs, labels = map(list, zip(*samples))
      batched_graph = dgl.batch(graphs)
      return batched_graph, torch.tensor(labels)
  
file_path: graph4nlp/graph4nlp/pytorch/test/graph_classification/run_graph_classification.py
imports: import argparse
  import dgl
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from dgl.data import MiniGCDataset
  from torch.utils.data import DataLoader
  from ...data.data import from_dgl, to_batch
  from ...modules.graph_embedding_learning.gat import GAT
  from ...modules.prediction.classification.graph_classification import FeedForwardNN
label_desc: Use image node_id 1 for calling a global function with example usage: collate(samples) and returns: batched_graph,  torch