____________________________________________________________________________________________________________________________________ test_normalize_total[direct] ____________________________________________________________________________________________________________________________________

adata = AnnData object with n_obs × n_vars = 10000 × 1000
    var: 'gene_ids', adata_dist = AnnData object with n_obs × n_vars = 10000 × 1000
    var: 'gene_ids'
    uns: 'dist-mode'

    def test_normalize_total(adata: AnnData, adata_dist: AnnData):
>       normalize_total(adata_dist)

scanpy/tests/test_preprocessing_distributed.py:93: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/home/phil/.local/share/hatch/env/virtual/scanpy/q4In3tK-/hatch-test.stable/lib/python3.12/site-packages/legacy_api_wrap/__init__.py:80: in fn_compatible
    return fn(*args_all, **kw)
scanpy/preprocessing/_normalization.py:240: in normalize_total
    adata, _normalize_data(X, counts_per_cell, target_sum), layer=layer
scanpy/preprocessing/_normalization.py:49: in _normalize_data
    return axis_mul_or_truediv(
/usr/lib/python3.12/functools.py:909: in wrapper
    return dispatch(args[0].__class__)(*args, **kw)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

X = <zappy.direct.array.DirectZappyArray object at 0x7f06eb607a40>, scaling_array = array([0.9270073, 0.4379562, 1.5985402, ..., 0.8540146, 1.5109489,
       0.8978102], dtype=float32), axis = 0, op = <built-in function truediv>

    @singledispatch
    def axis_mul_or_truediv(
        X: sparse.spmatrix,
        scaling_array,
        axis: Literal[0, 1],
        op: Callable[[Any, Any], Any],
        *,
        allow_divide_by_zero: bool = True,
        out: sparse.spmatrix | None = None,
    ) -> sparse.spmatrix:
        check_op(op)
        if out is not None:
            if X.data is not out.data:
                raise ValueError(
                    "`out` argument provided but not equal to X.  This behavior is not supported for sparse matrix scaling."
                )
        if not allow_divide_by_zero and op is truediv:
            scaling_array = scaling_array.copy() + (scaling_array == 0)
    
        row_scale = axis == 0
        column_scale = axis == 1
        if row_scale:
    
            def new_data_op(x):
                return op(x.data, np.repeat(scaling_array, np.diff(x.indptr)))
    
        elif column_scale:
    
            def new_data_op(x):
                return op(x.data, scaling_array.take(x.indices, mode="clip"))
    
>       if X.format == "csr":
E       AttributeError: 'DirectZappyArray' object has no attribute 'format'

scanpy/_utils/__init__.py:612: AttributeError