code: def create_dataloader(dataset, itemset, device):
      # Sample seed nodes from the itemset.
      datapipe = gb.ItemSampler(itemset, batch_size=16)
  
      # Copy the mini-batch to the designated device for sampling and training.
      datapipe = datapipe.copy_to(device, extra_attrs=["seed_nodes"])
  
      # Sample neighbors for the seed nodes.
      datapipe = datapipe.sample_neighbor(dataset.graph, fanouts=[4, 2])
  
      # Fetch features for sampled nodes.
      datapipe = datapipe.fetch_feature(
          dataset.feature, node_feature_keys=["feat"]
      )
  
      # Initiate the dataloader for the datapipe.
      return gb.DataLoader(datapipe)
  
file_path: dgl/examples/sampling/graphbolt/quickstart/node_classification.py
imports: import dgl.graphbolt
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
label_desc: Use image node_id 1 for calling a global function with example usage: create_dataloader(dataset, itemset, device) and returns: gb