def test_set_subset_varm(adata, subset_func):
    init_hash = joblib.hash(adata)
    orig_varm_val = adata.varm["o"].copy()

    while True:
        subset_idx = slice_subset(adata.var_names)
        if (adata[:, subset_idx]).shape[1] > 2:
            break
    subset = adata[:, subset_idx]

    internal_idx = _normalize_index(
        subset_func(np.arange(subset.varm["o"].shape[0])), subset.var_names
    )

    assert subset.is_view
    subset.varm["o"][internal_idx] = 1
    assert not subset.is_view
    assert np.all(adata.varm["o"] == orig_varm_val)

    assert init_hash == joblib.hash(adata)
def add_interim_values(module, input, output):
    """The forward hook used to save interim tensors, detached
    from the graph. Used to calculate the multipliers
    """
    try:
        del module.x
    except AttributeError:
        pass
    try:
        del module.y
    except AttributeError:
        pass
    module_type = module.__class__.__name__
    if module_type in op_handler:
        func_name = op_handler[module_type].__name__
        # First, check for cases where we don't need to save the x and y tensors
        if func_name == 'passthrough':
            pass
        else:
            # check only the 0th input varies
            for i in range(len(input)):
                if i != 0 and type(output) is tuple:
                    assert input[i] == output[i], "Only the 0th input may vary!"
            # if a new method is added, it must be added here too. This ensures tensors
            # are only saved if necessary
            if func_name in ['maxpool', 'nonlinear_1d']:
                # only save tensors if necessary
                if type(input) is tuple:
                    setattr(module, 'x', torch.nn.Parameter(input[0].detach()))
                else:
                    setattr(module, 'x', torch.nn.Parameter(input.detach()))
                if type(output) is tuple:
                    setattr(module, 'y', torch.nn.Parameter(output[0].detach()))
                else:
                    setattr(module, 'y', torch.nn.Parameter(output.detach()))
            if module_type in failure_case_modules:
                input[0].register_hook(deeplift_tensor_grad)