import xarray as xr
import dask

da = xr.DataArray(dask.array.array(""))
xr.zeros_like(da, dtype=bool)

/home/vsts/work/1/s/xarray/tests/test_duck_array_ops.py:499: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/home/vsts/work/1/s/xarray/testing.py:139: in compat_variable
    return a.dims == b.dims and (a._data is b._data or equiv(a.data, b.data))
/home/vsts/work/1/s/xarray/testing.py:36: in _data_allclose_or_equiv
    return duck_array_ops.array_equiv(arr1, arr2)
/home/vsts/work/1/s/xarray/core/duck_array_ops.py:246: in array_equiv
    flag_array = (arr1 == arr2) | (isnull(arr1) & isnull(arr2))
/home/vsts/work/1/s/xarray/core/duck_array_ops.py:104: in isnull
    return zeros_like(data, dtype=bool)
/home/vsts/work/1/s/xarray/core/duck_array_ops.py:56: in f
    return wrapped(*args, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/array/creation.py:174: in zeros_like
    return zeros(
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/array/wrap.py:78: in wrap_func_shape_as_first_arg
    return Array(dsk, name, chunks, dtype=dtype, meta=kwargs.get("meta", None))
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/array/core.py:1083: in __new__
    meta = meta_from_array(meta, dtype=dtype)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = array('', dtype='<U1'), ndim = 0, dtype = dtype('bool')

    def meta_from_array(x, ndim=None, dtype=None):
        """Normalize an array to appropriate meta object
    
        Parameters
        ----------
        x: array-like, callable
            Either an object that looks sufficiently like a Numpy array,
            or a callable that accepts shape and dtype keywords
    
        if dtype and meta.dtype != dtype:
>           meta = meta.astype(dtype)
E           ValueError: invalid literal for int() with base 10: ''