def METHOD_NAME(data, axis=0, order=1, mode="clip"):
    """
    Calculate the relative minima of `data`.
    Parameters
    ----------
    data : ndarray
        Array in which to find the relative minima.
    axis : int, optional
        Axis over which to select from `data`.  Default is 0.
    order : int, optional
        How many points on each side to use for the comparison
        to consider ``comparator(n, n+x)`` to be True.
    Returns
    -------
    extrema : tuple of ndarrays
        Indices of the minima in arrays of integers.  ``extrema[k]`` is
        the array of indices of axis `k` of `data`.  Note that the
        return value is a tuple even when `data` is one-dimensional.
    See Also
    --------
    argrelextrema, argrelmax, find_peaks
    Notes
    -----
    This function uses `argrelextrema` with cp.less as comparator. Therefore it
    requires a strict inequality on both sides of a value to consider it a
    minimum. This means flat minima (more than one sample wide) are not
    detected. In case of one-dimensional `data` `find_peaks` can be used to
    detect all local minima, including flat ones, by calling it with negated
    `data`.
    Examples
    --------
    >>> from cusignal import argrelmin
    >>> import cupy as cp
    >>> x = cp.array([2, 1, 2, 3, 2, 0, 1, 0])
    >>> argrelmin(x)
    (array([1, 5]),)
    >>> y = cp.array([[1, 2, 1, 2],
    ...               [2, 2, 0, 0],
    ...               [5, 3, 4, 4]])
    ...
    >>> argrelmin(y, axis=1)
    (array([0, 2]), array([2, 1]))
    """
    data = cp.asarray(data)
    return argrelextrema(data, cp.less, axis, order, mode)