code: def train(
      rank,
      torch_devices: List[int],
      manager_ip: str,
      manager_port: int,
      dask_scheduler_file: str,
      num_epochs: int,
      features_on_gpu=True,
  ) -> None:
      """
      Parameters
      ----------
      device: int
          The CUDA device where the model, graph data, and node labels will be stored.
      features_on_gpu: bool
          Whether to store a replica of features on each worker's GPU.  If False,
          all features will be stored on the CPU.
      """
  
      start_time_preprocess = time.perf_counter_ns()
  
      world_size = len(torch_devices)
      device_id = torch_devices[rank]
      features_device = device_id if features_on_gpu else "cpu"
      init_pytorch_worker(rank, torch_devices, manager_ip, manager_port)
      td.barrier()
  
      client = start_cugraph_dask_client(rank, dask_scheduler_file)
  
      from distributed import Event as Dask_Event
  
      event = Dask_Event("cugraph_store_creation_event")
      download_event = Dask_Event("dataset_download_event")
  
      td.barrier()
  
      import cugraph
      from cugraph_pyg.data import CuGraphStore
      from cugraph_pyg.loader import CuGraphNeighborLoader
  
      if rank == 0:
          print("Rank 0 downloading dataset")
          dataset = NodePropPredDataset(name="ogbn-mag")
          data = dataset[0]
          download_event.set()
          print("Dataset downloaded")
      else:
          if download_event.wait(timeout=1000):
              print(f"Rank {rank} loading dataset")
              dataset = NodePropPredDataset(name="ogbn-mag")
              data = dataset[0]
              print(f"Rank {rank} loaded dataset successfully")
  
      ei = data[0]["edge_index_dict"][("paper", "cites", "paper")]
      G = {
          ("paper", "cites", "paper"): np.stack(
              [
                  np.concatenate([ei[0], ei[1]]),
                  np.concatenate([ei[1], ei[0]]),
              ]
          )
      }
      N = {"paper": data[0]["num_nodes_dict"]["paper"]}
  
      fs = cugraph.gnn.FeatureStore(backend="torch")
  
      fs.add_data(
          torch.as_tensor(
              data[0]["node_feat_dict"]["paper"], device=features_device
          ),
          "paper",
          "x",
      )
  
      fs.add_data(
          torch.as_tensor(data[1]["paper"].T[0], device=device_id),
          "paper",
          "y",
      )
  
      num_papers = data[0]["num_nodes_dict"]["paper"]
  
      if rank == 0:
          train_perc = 0.1
          all_train_nodes = torch.randperm(num_papers)
          all_train_nodes = all_train_nodes[
              : int(train_perc * num_papers)
          ]
          train_nodes = all_train_nodes[
              : int(len(all_train_nodes) / world_size)
          ]
  
          train_mask = torch.full((num_papers,), -1, device=device_id)
          train_mask[train_nodes] = 1
          fs.add_data(train_mask, "paper", "train")
  
      print(f"Rank {rank} finished loading graph and feature data")
  
      if rank == 0:
          print(
              "Rank 0 creating its cugraph store and initializing distributed graph"
          )
          # Rank 0 will initialize the distributed cugraph graph.
          cugraph_store_create_start = time.perf_counter_ns()
          print("G:", G[("paper", "cites", "paper")].shape)
          cugraph_store = CuGraphStore(fs, G, N, multi_gpu=True)
          cugraph_store_create_end = time.perf_counter_ns()
          print(
              "cuGraph Store created on rank 0 in "
              f"{(cugraph_store_create_end - cugraph_store_create_start) / 1e9:3.4f} s"
          )
          client.publish_dataset(train_nodes=all_train_nodes)
          event.set()
          print("Rank 0 done with cugraph store creation")
      else:
          if event.wait(timeout=1000):
              print(f"Rank {rank} creating cugraph store")
              train_nodes = client.get_dataset("train_nodes")
              train_nodes = train_nodes[
                  int(rank * len(train_nodes) / world_size) : int(
                      (rank + 1) * len(train_nodes) / world_size
                  )
              ]
  
              train_mask = torch.full(
                  (num_papers,), -1, device=device_id
              )
              train_mask[train_nodes] = 1
              fs.add_data(train_mask, "paper", "train")
  
              # Will automatically use the stored distributed cugraph graph on rank 0.
              cugraph_store_create_start = time.perf_counter_ns()
              cugraph_store = CuGraphStore(fs, G, N, multi_gpu=True)
              cugraph_store_create_end = time.perf_counter_ns()
              print(
                  f"Rank {rank} created cugraph store in "
                  f"{(cugraph_store_create_end - cugraph_store_create_start) / 1e9:3.4f}"
                  " s"
              )
              print(f"Rank {rank} done with cugraph store creation")
  
      end_time_preprocess = time.perf_counter_ns()
      print(f"rank {rank}: train {train_nodes.shape}", flush=True)
      print(
          f"rank {rank}: all preprocessing took"
          f" {(end_time_preprocess - start_time_preprocess) / 1e9:3.4f}",
          flush=True,
      )
      td.barrier()
      model = (
          CuGraphSAGE(
              in_channels=128,
              hidden_channels=64,
              out_channels=349,
              num_layers=3,
          )
          .to(torch.float32)
          .to(device_id)
      )
      model = ddp(
          model, device_ids=[device_id], output_device=device_id
      )
      td.barrier()
  
      optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
  
      for epoch in range(num_epochs):
          start_time_train = time.perf_counter_ns()
          model.train()
  
          start_time_loader = time.perf_counter_ns()
          cugraph_bulk_loader = CuGraphNeighborLoader(
              cugraph_store,
              train_nodes,
              batch_size=250,
              num_neighbors=[10, 10, 10],
              seeds_per_call=1000,
              batches_per_partition=2,
              replace=False,
          )
          end_time_loader = time.perf_counter_ns()
          total_time_loader = (
              end_time_loader - start_time_loader
          ) / 1e9
  
          total_loss = 0
          num_batches = 0
  
          print(f"rank {rank} starting epoch {epoch}")
          with td.algorithms.join.Join([model]):
              total_time_sample = 0
              total_time_forward = 0
              total_time_backward = 0
  
              start_time_sample = time.perf_counter_ns()
              for iter_i, hetero_data in enumerate(cugraph_bulk_loader):
                  end_time_sample = time.perf_counter_ns()
                  total_time_sample += (
                      end_time_sample - start_time_sample
                  ) / 1e9
                  num_batches += 1
  
                  if iter_i % 20 == 0:
                      print(f"iteration {iter_i}")
  
                  # train
                  train_mask = hetero_data.train_dict["paper"]
                  y_true = hetero_data.y_dict["paper"]
  
                  start_time_forward = time.perf_counter_ns()
                  y_pred = model(
                      hetero_data.x_dict["paper"]
                      .to(device_id)
                      .to(torch.float32),
                      hetero_data.edge_index_dict[
                          ("paper", "cites", "paper")
                      ].to(device_id),
                      (len(y_true), len(y_true)),
                  )
                  end_time_forward = time.perf_counter_ns()
                  total_time_forward += (
                      end_time_forward - start_time_forward
                  ) / 1e9
  
                  y_true = F.one_hot(
                      y_true[train_mask].to(torch.int64),
                      num_classes=349,
                  ).to(torch.float32)
  
                  y_pred = y_pred[train_mask]
  
                  loss = F.cross_entropy(y_pred, y_true)
  
                  start_time_backward = time.perf_counter_ns()
                  optimizer.zero_grad()
                  loss.backward()
                  optimizer.step()
                  end_time_backward = time.perf_counter_ns()
                  total_time_backward += (
                      end_time_backward - start_time_backward
                  ) / 1e9
  
                  total_loss += loss.item()
  
                  del y_true
                  del y_pred
                  del loss
                  del hetero_data
                  gc.collect()
  
                  start_time_sample = time.perf_counter_ns()
  
              end_time_train = time.perf_counter_ns()
              print(
                  f"epoch {epoch} "
                  f"total time: {(end_time_train - start_time_train) / 1e9:3.4f} s"
                  f"\nloader create time per batch: {total_time_loader / num_batches} s"
                  f"\nsampling/load time per batch: {total_time_sample / num_batches} s"
                  f"\nforward time per batch: {total_time_forward / num_batches} s"
                  f"\nbackward time per batch: {total_time_backward / num_batches} s"
                  f"\nnum batches: {num_batches}"
              )
              print(
                  f"loss after epoch {epoch}: {total_loss / num_batches}"
              )
  
      td.barrier()
      if rank == 0:
          print("DONE", flush=True)
          client.unpublish_dataset("train_nodes")
          event.clear()
  
      td.destroy_process_group()
  
imports: from ogb.nodeproppred import NodePropPredDataset
  import time
  import argparse
  import gc
  import torch
  import numpy
  from torch_geometric.nn import CuGraphSAGEConv
  import torch.nn
  import torch.nn.functional
  import torch.distributed
  import torch.multiprocessing
  from torch.nn.parallel import DistributedDataParallel
  from typing import List
label_desc: Use image node_id 5 for calling a global function with example usage: train(rank, torch_devices, manager_ip, manager_port, dask_scheduler_file, num_epochs, features_on_gpu) without return types