code: def run_inference(
      dataset,
      model,
      epochs,
      profiling,
      bf16,
      use_compile,
      permute_masks=None,
      logger=None,
  ):
      data = dataset[0]
      if permute_masks is not None:
          data = permute_masks(data, dataset.num_classes)
      data = data.to(device)
  
      model.to(device).reset_parameters()
      if use_compile:
          model = torch.compile(model)
  
      if torch.cuda.is_available():
          amp = torch.cuda.amp.autocast(enabled=False)
      else:
          amp = torch.cpu.amp.autocast(enabled=bf16)
      if bf16:
          data.x = data.x.to(torch.bfloat16)
  
      with amp:
          for epoch in range(1, epochs + 1):
              if epoch == epochs:
                  with timeit():
                      inference(model, data)
              else:
                  inference(model, data)
  
          if profiling:
              with torch_profile():
                  inference(model, data)
  
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 3 for calling a global function with example usage: run_inference(dataset, model, epochs, profiling, bf16, use_compile, permute_masks, logger) without return types