code: def e2e_benchmark(
      g, feat, y, train_idx, fanouts, batch_sizes, use_uva
  ):
      """
      Run the e2e_benchmark
      Args:
          g: DGLGraph
          feat: Tensor containing the features.
          y: Tensor containing the labels.
          train_idx: Tensor containing the training indices.
          fanouts: List of fanouts to use for the dataloader.
          batch_sizes: List of batch sizes to use for the dataloader.
          use_uva: Whether to use unified virtual address space.
          model_backend: Backend of model to use.
      """
      time_ls = []
      for fanout in fanouts:
          for batch_size in batch_sizes:
              dataloader = create_dataloader(
                  g, train_idx, batch_size, fanout, use_uva
              )
              time_d = run_1_epoch(
                  dataloader,
                  feat,
                  y,
                  fanout,
                  batch_size,
                  model_backend="dgl",
              )
              time_ls.append(time_d)
              print("=" * 30)
      return time_ls
  
file_path: cugraph/benchmarks/cugraph-dgl/scale-benchmarks/dgl_benchmark.py
imports: import json
  import os
  import time
  import dgl
  from dgl.dataloading import MultiLayerNeighborSampler, DataLoader
  import pandas
  import torch
  from model import run_1_epoch
  from argparse import ArgumentParser
  from load_graph_feats import load_edges_from_disk, load_node_labels, load_node_features
label_desc: Use image node_id 4 for calling a global function with example usage: e2e_benchmark(g, feat, y, train_idx, fanouts, batch_sizes, use_uva) and returns: time_ls