human
Can you provide a the Python code you requested given the following:
Complete the below
def concatenate3(arrays):

    ''''Recursive np.concatenate
    Input should be a nested list of numpy arrays arranged in the order they
    should appear in the array itself.  Each array should have the same number
    of dimensions as the desired output and the nesting of the lists.
    >>> x = np.array([[1, 2]])
    >>> concatenate3([[x, x, x], [x, x, x]])
    array([[1, 2, 1, 2, 1, 2],
    [1, 2, 1, 2, 1, 2]])
    >>> concatenate3([[x, x], [x, x], [x, x]])
    array([[1, 2, 1, 2],
    [1, 2, 1, 2],
    [1, 2, 1, 2]])'
    '''
assistant
#Here is the Python code you requested.
    arrays = concrete(arrays)
    if (not arrays):
        return np.empty(0)
    advanced = max(core.flatten(arrays, container=(list, tuple)), key=(lambda x: getattr(x, '__array_priority__', 0)))
    module = (package_of(type(advanced)) or np)
    if ((module is not np) and hasattr(module, 'concatenate')):
        x = unpack_singleton(arrays)
        return _concatenate2(arrays, axes=list(range(x.ndim)))
    ndim = ndimlist(arrays)
    if (not ndim):
        return arrays
    chunks = chunks_from_arrays(arrays)
    shape = tuple(map(sum, chunks))
    def dtype(x):
        try:
            return x.dtype
        except AttributeError:
            return type(x)
    result = np.empty(shape=shape, dtype=dtype(deepfirst(arrays)))
    for (idx, arr) in zip(slices_from_chunks(chunks), core.flatten(arrays)):
        if hasattr(arr, 'ndim'):
            while (arr.ndim < ndim):
                arr = arr[None, ...]
        result[idx] = arr
    return result