code: def create_dataloader(A, fanouts, ids, features, device):
      datapipe = gb.ItemSampler(ids, batch_size=1024)
      # Customize graphbolt sampler by sparse.
      datapipe = datapipe.sample_sparse_neighbor(A, fanouts)
      # Use grapbolt to fetch features.
      datapipe = datapipe.fetch_feature(
          features, node_feature_keys=["feat"]
      )
      datapipe = datapipe.copy_to(device)
      dataloader = gb.DataLoader(datapipe)
      return dataloader
  
imports: import argparse
  from functools import partial
  import dgl.graphbolt
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  from dgl.graphbolt.subgraph_sampler import SubgraphSampler
  from torch.utils.data import functional_datapipe
label_desc: Use image node_id 1 for calling a global function with example usage: create_dataloader(A, fanouts, ids, features, device) and returns: dataloader