def checkpoint_wrapper(m, offload_to_cpu=False):
    """
    A friendlier wrapper for performing activation checkpointing.

    Compared to the PyTorch version, this version:
    - wraps an nn.Module, so that all subsequent calls will use checkpointing
    - handles keyword arguments in the forward
    - handles non-Tensor outputs from the forward

    Usage::

        checkpointed_module = checkpoint_wrapper(my_module, offload_to_cpu=True)
        a, b = checkpointed_module(x, y=3, z=torch.Tensor([1]))
    """
    # should I check whether original_forward has already been set?
    assert not hasattr(
        m, "precheckpoint_forward"
    ), "checkpoint function has already been applied?"
    m.precheckpoint_forward = m.forward
    m.forward = functools.partial(
        _checkpointed_forward,
        m.precheckpoint_forward,  # original_forward
        offload_to_cpu,
    )
    return m

    A friendlier wrapper for performing activation checkpointing.

    Compared to the PyTorch version, this version:
    - wraps an nn.Module, so that all subsequent calls will use checkpointing
    - handles keyword arguments in the forward
    - handles non-Tensor outputs from the forward

    Usage::

        checkpointed_module = checkpoint_wrapper(my_module, offload_to_cpu=True)
        a, b = checkpointed_module(x, y=3, z=torch.Tensor([1]))
    
structured_prompting/fairseq-version/fairseq/fairseq/modules/checkpoint_activations.py