issue_comments: 922942743

I wrote a very naive fix, it works but seems to perform really slowly, I would appreciate some feedback (I'm a beginner with Dask). Basically, I added k = dask.array.asarray(k, dtype=np.int64) to do the exact same thing as with numpy. I can create a PR if it's better to review this

The patch: ``` class VectorizedIndexer(ExplicitIndexer): """Tuple for vectorized indexing.

All elements should be slice or N-dimensional np.ndarray objects with an
integer dtype and the same number of dimensions. Indexing follows proposed
rules for np.ndarray.vindex, which matches NumPy's advanced indexing rules
(including broadcasting) except sliced axes are always moved to the end:
https://github.com/numpy/numpy/pull/6256
"""

__slots__ = ()

def __init__(self, key):
    if not isinstance(key, tuple):
        raise TypeError(f"key must be a tuple: {key!r}")

    new_key = []
    ndim = None
    for k in key:
        if isinstance(k, slice):
            k = as_integer_slice(k)
        elif isinstance(k, np.ndarray) or isinstance(k, dask.array.Array):
            if not np.issubdtype(k.dtype, np.integer):
                raise TypeError(
                    f"invalid indexer array, does not have integer dtype: {k!r}"
                )
            if ndim is None:
                ndim = k.ndim
            elif ndim != k.ndim:
                ndims = [k.ndim for k in key if isinstance(k, np.ndarray)]
                raise ValueError(
                    "invalid indexer key: ndarray arguments "
                    f"have different numbers of dimensions: {ndims}"
                )
            if isinstance(k, dask.array.Array):
                k = dask.array.asarray(k, dtype=np.int64)
            else:
                k = np.asarray(k, dtype=np.int64)
        else:
            raise TypeError(
                f"unexpected indexer type for {type(self).__name__}: {k!r}"
            )
        new_key.append(k)

    super().__init__(new_key)

```