<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def multi_indexing(data, i, indexing=None):
 """Perform indexing on multiple data structures. Currently supported data types: * numpy arrays * torch tensors * pandas NDFrame * a dictionary of the former three * a list/tuple of the former three ``i`` can be an integer or a slice. Examples -------- 1 array([1, 2]) tensor([ 1., 2.]) [[1, 2], [4, 5]] {'a': [2, 3], 'b': [5, 6]} a b 1 2 5 2 3 6 Parameters data Data of a type mentioned above. i : int or slice Slicing index. indexing : function/callable or None (default=None) If not None, use this function for indexing into the data. If None, try to automatically determine how to index data. """

    # in case of i being a numpy array
    i = _normalize_numpy_indices(i)

    # If we already know how to index, use that knowledge
    if indexing is not None:
        return indexing(data, i)

    # If we don't know how to index, find out and apply
    return check_indexing(data)(data, i)