code: def run(
      train_dataset,
      test_dataset,
      model,
      epochs,
      batch_size,
      lr,
      lr_decay_factor,
      lr_decay_step_size,
      weight_decay,
      inference,
      profiling,
      bf16,
      use_compile,
  ):
      if not inference:
          run_train(
              train_dataset,
              test_dataset,
              model,
              epochs,
              batch_size,
              use_compile,
              lr,
              lr_decay_factor,
              lr_decay_step_size,
              weight_decay,
          )
      else:
          run_inference(
              test_dataset,
              model,
              epochs,
              batch_size,
              profiling,
              bf16,
              use_compile,
          )
  
file_path: pytorch_geometric/benchmark/points/train_eval.py
imports: import time
  import torch
  import torch.nn.functional
  from torch.optim import Adam
  from torch_geometric.loader import DataLoader
  from torch_geometric.profile import timeit, torch_profile
label_desc: Use image node_id 3 for calling a global function with example usage: run(train_dataset, test_dataset, model, epochs, batch_size, lr, lr_decay_factor, lr_decay_step_size, weight_decay, inference, profiling, bf16, use_compile) without return types