code: def run(
      dataset,
      model,
      runs,
      epochs,
      lr,
      weight_decay,
      early_stopping,
      inference,
      profiling,
      bf16,
      use_compile,
      permute_masks=None,
      logger=None,
  ):
      if not inference:
          run_train(
              dataset,
              model,
              runs,
              epochs,
              lr,
              weight_decay,
              early_stopping,
              profiling,
              use_compile,
              permute_masks,
              logger,
          )
      else:
          run_inference(
              dataset,
              model,
              epochs,
              profiling,
              bf16,
              use_compile,
              permute_masks,
              logger,
          )
  
file_path: pytorch_geometric/benchmark/citation/train_eval.py
imports: import time
  import torch
  import torch.nn.functional
  from torch import tensor
  from torch.optim import Adam
  from torch_geometric.profile import timeit, torch_profile
  from torch_geometric.utils import index_to_mask
label_desc: Use image node_id 4 for calling a global function with example usage: run(dataset, model, runs, epochs, lr, weight_decay, early_stopping, inference, profiling, bf16, use_compile, permute_masks, logger) without return types