def test_set_varm(adata):
    init_hash = joblib.hash(adata)

    dim0_size = np.random.randint(2, adata.shape[1] - 1)
    dim1_size = np.random.randint(1, 99)
    orig_varm_val = adata.varm["o"].copy()
    subset_idx = np.random.choice(adata.var_names, dim0_size, replace=False)

    subset = adata[:, subset_idx]
    assert subset.is_view
    subset.varm = dict(o=np.ones((dim0_size, dim1_size)))
    assert not subset.is_view
    assert np.all(orig_varm_val == adata.varm["o"])  # Checking for mutation
    assert np.all(subset.varm["o"] == np.ones((dim0_size, dim1_size)))

    subset = adata[:, subset_idx]
    subset_hash = joblib.hash(subset)
    with pytest.raises(ValueError):
        subset.varm = dict(o=np.ones((dim0_size + 1, dim1_size)))
    with pytest.raises(ValueError):
        subset.varm = dict(o=np.ones((dim0_size - 1, dim1_size)))
    # subset should not be changed by failed setting
    assert subset_hash == joblib.hash(subset)
    assert init_hash == joblib.hash(adata)
def __init__(self, train_x, train_y, likelihood):
        super(GPRegressionModel, self).__init__(train_x, train_y, likelihood)
        self.mean_module = ConstantMean(prior=SmoothedBoxPrior(-1e-5, 1e-5))
        self.base_covar_module = ScaleKernel(RBFKernel(lengthscale_prior=SmoothedBoxPrior(exp(-5), exp(6), sigma=0.1)))
        self.covar_module = InducingPointKernel(
            self.base_covar_module, inducing_points=torch.linspace(0, 1, 32), likelihood=likelihood
        )