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 = make_checkpoint(f, x, 0)
      y.backward()
  
      assert a.grad is not None
  
file_path: fairscale/tests/nn/pipe/test_checkpoint.py
imports: from functools import partial
  import pytest
  import torch
  from torch import nn
  import torch.cuda
  from fairscale.nn.pipe.checkpoint import Checkpointing, Function, TensorOrTensors, is_checkpointing, is_recomputing
  from fairscale.nn.pipe.dependency import fork, join
  from fairscale.nn.pipe.microbatch import Batch
label_desc: Use image node_id 4 for calling a global function with example usage: test_not_requires_grad_with_parameter() and returns: unknown