code: def test_detect_checkpointing_recomputing_without_checkpoint():
      logs = []
  
      class Detect(nn.Module):
          def forward(self, input):
              logs.append((is_checkpointing(), is_recomputing()))
              return input
  
      model = Detect()
      input = torch.rand(1, requires_grad=True)
  
      output = model(input)
      output.backward()
  
      assert logs == [(False, False)]
  
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 7 for calling a global function with example usage: test_detect_checkpointing_recomputing_without_checkpoint() and returns: input