issues

What happened:

Defining the kwargs used in xr.apply_ufunc in a separate dictionary and using it multiple times in different call of the method, while using dask="paralellized", ends in an error since the dimension names in ouput_sizes (inside dask_gufunc_kwargs) are modified internally.

What you expected to happen:

Keep the same dictionary of kwargs unmodified

Minimal Complete Verifiable Example:

```python import numpy as np

import xarray as xr

def dummy1(data, nfft): return data[..., (nfft // 2) + 1 :] * 2

def dummy2(data, nfft): return data[..., (nfft // 2) + 1 :] / 2

def xoperations(xarr, **kwargs): ufunc_kwargs = dict( kwargs=kwargs, input_core_dims=[["time"]], output_core_dims=[["freq"]], dask="parallelized", output_dtypes=[np.float], dask_gufunc_kwargs=dict(output_sizes={"freq": int(kwargs["nfft"] / 2) + 1}), )

ans1 = xr.apply_ufunc(dummy1, xarr, **ufunc_kwargs)
ans2 = xr.apply_ufunc(dummy2, xarr, **ufunc_kwargs)

return ans1, ans2

test = xr.DataArray( 4, coords=[("time", np.arange(1000)), ("lon", np.arange(160, 300, 10))] ).chunk({"time": -1, "lon": 10})

xoperations(test, nfft=1024) ```

This returns

```

ValueError Traceback (most recent call last) <ipython-input-1-822bd3b2d4da> in <module> 32 ).chunk({"time": -1, "lon": 10}) 33 ---> 34 xoperations(test, nfft=1024)

<ipython-input-1-822bd3b2d4da> in xoperations(xarr, kwargs) 23 24 ans1 = xr.apply_ufunc(dummy1, xarr, ufunc_kwargs) ---> 25 ans2 = xr.apply_ufunc(dummy2, xarr, **ufunc_kwargs) 26 27 return ans1, ans2

~/GitLab/xarray_test/xarray/xarray/core/computation.py in apply_ufunc(func, input_core_dims, output_core_dims, exclude_dims, vectorize, join, dataset_join, dataset_fill_value, keep_attrs, kwargs, dask, output_dtypes, output_sizes, meta, dask_gufunc_kwargs, *args) 1086 join=join, 1087 exclude_dims=exclude_dims, -> 1088 keep_attrs=keep_attrs, 1089 ) 1090 # feed Variables directly through apply_variable_ufunc

~/GitLab/xarray_test/xarray/xarray/core/computation.py in apply_dataarray_vfunc(func, signature, join, exclude_dims, keep_attrs, args) 260 261 data_vars = [getattr(a, "variable", a) for a in args] --> 262 result_var = func(data_vars) 263 264 if signature.num_outputs > 1:

~/GitLab/xarray_test/xarray/xarray/core/computation.py in apply_variable_ufunc(func, signature, exclude_dims, dask, output_dtypes, vectorize, keep_attrs, dask_gufunc_kwargs, *args) 632 if key not in signature.all_output_core_dims: 633 raise ValueError( --> 634 f"dimension '{key}' in 'output_sizes' must correspond to output_core_dims" 635 ) 636 output_sizes_renamed[signature.dims_map[key]] = value

ValueError: dimension 'dim0' in 'output_sizes' must correspond to output_core_dims ```

It is easily verifiable by sneaking a print statement before and after calling the first apply_ufunc. Everything is the same but the dimension names in output_sizes

```python {'kwargs': {'nfft': 1024}, 'input_core_dims': [['time']], 'output_core_dims': [['freq']], 'dask': 'parallelized', 'output_dtypes': [<class 'float'>], 'dask_gufunc_kwargs': {'output_sizes': {'freq': 513}}} {'kwargs': {'nfft': 1024}, 'input_core_dims': [['time']], 'output_core_dims': [['freq']], 'dask': 'parallelized', 'output_dtypes': [<class 'float'>], 'dask_gufunc_kwargs': {'output_sizes': {'dim0': 513}}}

```

Anything else we need to know?:

I have a fork with a fix ready to be sent as a PR. I just imported the copy module and used deepcopy like this

python dask_gufunc_kwargs = copy.deepcopy(dask_gufunc_kwargs) around here

https://github.com/pydata/xarray/blob/2acd0fc6563c3ad57f16e6ee804d592969419938/xarray/core/computation.py#L1013-L1020

If it's good enough then I can send the PR.

Environment: