<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def normalized_axes_tuple(axes, ndim):
 """Return a tuple of ``axes`` converted to positive integers. This function turns negative entries into equivalent positive ones according to standard Python indexing "from the right". Parameters axes : int or sequence of ints Single integer or integer sequence of arbitrary length. Duplicate entries are not allowed. All entries must fulfill ``-ndim <= axis <= ndim - 1``. ndim : positive int Number of available axes determining the valid axis range. Returns ------- axes_list : tuple of ints The converted tuple of axes. Examples -------- Normalizing a sequence of axes: (0, 2, 2) Single integer works, too: (0,) """

    try:
        axes, axes_in = (int(axes),), axes
    except TypeError:
        axes, axes_in = tuple(int(axis) for axis in axes), axes
        if any(axis != axis_in for axis, axis_in in zip(axes, axes_in)):
            raise ValueError('`axes` may only contain integers, got {}'
                             ''.format(axes_in))
    else:
        if axes[0] != axes_in:
            raise TypeError('`axes` must be integer or sequence, got {}'
                            ''.format(axes_in))

    if len(set(axes)) != len(axes):
        raise ValueError('`axes` may not contain duplicate entries')

    ndim, ndim_in = int(ndim), ndim
    if ndim <= 0:
        raise ValueError('`ndim` must be positive, got {}'.format(ndim_in))

    axes_arr = np.array(axes)
    axes_arr[axes_arr < 0] += ndim
    if np.any((axes_arr < 0) | (axes_arr >= ndim)):
        raise ValueError('all `axes` entries must satisfy -{0} <= axis < {0}, '
                         'got {1}'.format(ndim, axes_in))

    return tuple(axes_arr)