def tsqr(a):
    """Perform a QR decomposition of a tall-skinny matrix.

    Args:
        a: A distributed matrix with shape MxN (suppose K = min(M, N)).

    Returns:
        A tuple of q (a DistArray) and r (a numpy array) satisfying the
            following.
            - If q_full = ray.get(DistArray, q).assemble(), then
              q_full.shape == (M, K).
            - np.allclose(np.dot(q_full.T, q_full), np.eye(K)) == True.
            - If r_val = ray.get(np.ndarray, r), then r_val.shape == (K, N).
            - np.allclose(r, np.triu(r)) == True.
    """
    if len(a.shape) != 2:
        raise Exception("tsqr requires len(a.shape) == 2, but a.shape is "
                        "{}".format(a.shape))
    if a.num_blocks[1] != 1:
        raise Exception("tsqr requires a.num_blocks[1] == 1, but a.num_blocks "
                        "is {}".format(a.num_blocks))

    num_blocks = a.num_blocks[0]
    K = int(np.ceil(np.log2(num_blocks))) + 1
    q_tree = np.empty((num_blocks, K), dtype=object)
    current_rs = []
    for i in range(num_blocks):
        block = a.objectids[i, 0]
        q, r = ra.linalg.qr.remote(block)
        q_tree[i, 0] = q
        current_rs.append(r)
    for j in range(1, K):
        new_rs = []
        for i in range(int(np.ceil(1.0 * len(current_rs) / 2))):
            stacked_rs = ra.vstack.remote(*current_rs[(2 * i):(2 * i + 2)])
            q, r = ra.linalg.qr.remote(stacked_rs)
            q_tree[i, j] = q
            new_rs.append(r)
        current_rs = new_rs
    assert len(current_rs) == 1, "len(current_rs) = " + str(len(current_rs))

    # handle the special case in which the whole DistArray "a" fits in one
    # block and has fewer rows than columns, this is a bit ugly so think about
    # how to remove it
    if a.shape[0] >= a.shape[1]:
        q_shape = a.shape
    else:
        q_shape = [a.shape[0], a.shape[0]]
    q_num_blocks = core.DistArray.compute_num_blocks(q_shape)
    q_objectids = np.empty(q_num_blocks, dtype=object)
    q_result = core.DistArray(q_shape, q_objectids)

    # reconstruct output
    for i in range(num_blocks):
        q_block_current = q_tree[i, 0]
        ith_index = i
        for j in range(1, K):
            if np.mod(ith_index, 2) == 0:
                lower = [0, 0]
                upper = [a.shape[1], core.BLOCK_SIZE]
            else:
                lower = [a.shape[1], 0]
                upper = [2 * a.shape[1], core.BLOCK_SIZE]
            ith_index //= 2
            q_block_current = ra.dot.remote(
                q_block_current,
                ra.subarray.remote(q_tree[ith_index, j], lower, upper))
        q_result.objectids[i] = q_block_current
    r = current_rs[0]
    return q_result, ray.get(r)
Perform a QR decomposition of a tall-skinny matrix.

    Args:
        a: A distributed matrix with shape MxN (suppose K = min(M, N)).

    Returns:
        A tuple of q (a DistArray) and r (a numpy array) satisfying the
            following.
            - If q_full = ray.get(DistArray, q).assemble(), then
              q_full.shape == (M, K).
            - np.allclose(np.dot(q_full.T, q_full), np.eye(K)) == True.
            - If r_val = ray.get(np.ndarray, r), then r_val.shape == (K, N).
            - np.allclose(r, np.triu(r)) == True.