def test_view_of_view(matrix_type, subset_func, subset_func2):
    adata = gen_adata((30, 15), X_type=matrix_type)
    adata.raw = adata
    if subset_func is single_subset:
        pytest.xfail("Other subset generating functions have trouble with this")
    var_s1 = subset_func(adata.var_names, min_size=4)
    var_view1 = adata[:, var_s1]
    var_s2 = subset_func2(var_view1.var_names)
    var_view2 = var_view1[:, var_s2]
    assert var_view2._adata_ref is adata
    obs_s1 = subset_func(adata.obs_names, min_size=4)
    obs_view1 = adata[obs_s1, :]
    obs_s2 = subset_func2(obs_view1.obs_names)
    assert adata[obs_s1, :][:, var_s1][obs_s2, :]._adata_ref is adata

    view_of_actual_copy = adata[:, var_s1].copy()[obs_s1, :].copy()[:, var_s2].copy()

    view_of_view_copy = adata[:, var_s1][obs_s1, :][:, var_s2].copy()

    assert_equal(view_of_actual_copy, view_of_view_copy, exact=True)
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)