code: def create_dataloader(gs, train_idx, device):
      import cugraph_dgl
  
      temp_dir = tempfile.TemporaryDirectory()
      sampler = cugraph_dgl.dataloading.NeighborSampler([10, 20])
      dataloader = cugraph_dgl.dataloading.DataLoader(
          gs,
          train_idx,
          sampler,
          sampling_output_dir=temp_dir.name,
          batches_per_partition=10,
          device=device,  # Put the sampled MFGs on CPU or GPU
          use_ddp=True,  # Make it work with distributed data parallel
          batch_size=1024,
          shuffle=False,  # Whether to shuffle the nodes for every epoch
          drop_last=False,
          num_workers=0,
      )
      return dataloader
  
file_path: cugraph/python/cugraph-dgl/examples/multi_trainer_MG_example/workflow.py
imports: import dgl
  import torch
  import time
  from distributed import Client, Event
  import tempfile
  from cugraph.dask.comms import comms
label_desc: Use image node_id 7 for calling a global function with example usage: create_dataloader(gs, train_idx, device) and returns: dataloader