code: def test_not_requires_grad_with_parameter():
      x = torch.rand(1, requires_grad=False)
      a = torch.rand(1, requires_grad=True)
  
      def f(x):
          return x * a
  
      y = checkpoint(f, x)
      y.backward()
  
      assert a.grad is not None
  
file_path: pytorch/test/distributed/pipeline/sync/test_checkpoint.py
imports: from functools import partial
  import pytest
  import torch
  from torch import nn
  import torch.cuda
  from torch.distributed.pipeline.sync.checkpoint import Checkpointing, checkpoint, is_checkpointing, is_recomputing
  from torch.distributed.pipeline.sync.dependency import fork, join
  from torch.distributed.pipeline.sync.microbatch import Batch
  from torch.testing._internal.common_utils import run_tests
label_desc: Use image node_id 3 for calling a global function with example usage: test_not_requires_grad_with_parameter() and returns: unknown