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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def concat(mats):
 """Concatenate Matrix objects. Tries either axis. Parameters mats: list list of Matrix objects Returns ------- Matrix : Matrix """

    for mat in mats:
        if mat.isdiagonal:
            raise NotImplementedError("concat not supported for diagonal mats")

    row_match = True
    col_match = True
    for mat in mats[1:]:
        if sorted(mats[0].row_names) != sorted(mat.row_names):
            row_match = False
        if sorted(mats[0].col_names) != sorted(mat.col_names):
            col_match = False
    if not row_match and not col_match:
        raise Exception("mat_handler.concat(): all Matrix objects"+\
                        "must share either rows or cols")

    if row_match and col_match:
        raise Exception("mat_handler.concat(): all Matrix objects"+\
                        "share both rows and cols")

    if row_match:
        row_names = copy.deepcopy(mats[0].row_names)
        col_names = []
        for mat in mats:
            col_names.extend(copy.deepcopy(mat.col_names))
        x = mats[0].newx.copy()
        for mat in mats[1:]:
            mat.align(mats[0].row_names, axis=0)
            other_x = mat.newx
            x = np.append(x, other_x, axis=1)

    else:
        col_names = copy.deepcopy(mats[0].col_names)
        row_names = []
        for mat in mats:
            row_names.extend(copy.deepcopy(mat.row_names))
        x = mats[0].newx.copy()
        for mat in mats[1:]:
            mat.align(mats[0].col_names, axis=1)
            other_x = mat.newx
            x = np.append(x, other_x, axis=0)
    return Matrix(x=x, row_names=row_names, col_names=col_names)