id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
1171916710,I_kwDOAMm_X85F2gem,6372,"apply_ufunc + dask=""parallelized"" + no core dimensions should raise a nicer error about core dimensions being absent",2448579,open,0,,,0,2022-03-17T04:25:37Z,2022-03-17T05:10:16Z,,MEMBER,,,,"### What happened?

From https://github.com/pydata/xarray/discussions/6370


Calling `apply_ufunc(..., dask=""parallelized"")` with no core dimensions and dask input ""works"" but raises an error on compute (`ValueError: axes don't match array` from `np.transpose`).

``` python
xr.apply_ufunc(
    lambda x: np.mean(x),
    dt,
    dask=""parallelized""
)
```

### What did you expect to happen?

With numpy data the apply_ufunc call does raise an error:

```
xr.apply_ufunc(
    lambda x: np.mean(x),
    dt.compute(),
    dask=""parallelized""
)
```

``` 
ValueError: applied function returned data with unexpected number of dimensions. Received 0 dimension(s) but expected 1 dimensions with names: ('x',)
```

### Minimal Complete Verifiable Example

``` python
import xarray as xr

dt = xr.Dataset(
    data_vars=dict(
        value=([""x""], [1,1,2,2,2,3,3,3,3,3]), 
    ),
    coords=dict(
        lon=([""x""], np.linspace(0,1,10)),
    ),
).chunk(chunks={'x': tuple([2,3,5])}) # three chunks of different size

xr.apply_ufunc(
    lambda x: np.mean(x),
    dt,
    dask=""parallelized""
)
```

### Relevant log output

_No response_

### Anything else we need to know?

_No response_

### Environment

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