issue_comments: 364529325

I figured out how to consolidate two vectorized indexers, as long as they don't include any slice objects: ```python import numpy as np

def index_vectorized_indexer(old_indexer, applied_indexer): return tuple(o[applied_indexer] for o in np.broadcast_arrays(*old_indexer))

for x, old, applied in [ (np.arange(10), (np.arange(2, 7),), (np.array([3, 2, 1]),)), (np.arange(10), (np.arange(6).reshape(2, 3),), (np.arange(2), np.arange(1, 3))), (-np.arange(1, 21).reshape(4, 5), (np.arange(3)[:, None], np.arange(4)[None, :]), (np.arange(3), np.arange(3))), ]: new_key = index_vectorized_indexer(old, applied) np.testing.assert_array_equal(x[old][applied], x[new_key]) ```

We could probably make this work with VectorizedIndexer if we converted the slice objects to arrays. I think we might even already have some code to do that conversion somewhere. So another option would be to convert BasicIndexer and OuterIndexer -> VectorizedIndexer if necessary and then use this path.