code: def create_dataloader(
      sampled_dir, total_num_nodes, sparse_format, return_type
  ):
      print("Creating dataloader", flush=True)
      st = time.time()
      dataset = HomogenousBulkSamplerDataset(
          total_num_nodes,
          edge_dir="in",
          sparse_format=sparse_format,
          return_type=return_type,
      )
  
      dataset.set_input_files(sampled_dir)
      dataloader = torch.utils.data.DataLoader(
          dataset,
          collate_fn=lambda x: x,
          shuffle=False,
          num_workers=0,
          batch_size=None,
      )
      et = time.time()
      print(
          f"Time to create dataloader = {et - st:.2f} seconds",
          flush=True,
      )
      return dataloader
  
file_path: cugraph/benchmarks/cugraph-dgl/scale-benchmarks/cugraph_dgl_benchmark.py
imports: import os
  import json
  import pandas
  import os
  import time
  from rmm.allocators.torch import rmm_torch_allocator
  import rmm
  import torch
  from cugraph_dgl.dataloading import HomogenousBulkSamplerDataset
  from model import run_1_epoch
  from argparse import ArgumentParser
  from load_graph_feats import load_node_labels, load_node_features
label_desc: Use image node_id 1 for calling a global function with example usage: create_dataloader(sampled_dir, total_num_nodes, sparse_format, return_type) and returns: dataloader