def argmin(
    a: ndarray,
    axis: int | None = None,
    out: ndarray | None = None,
    *,
    keepdims: bool = False,
) -> ndarray:
    """

    Returns the indices of the minimum values along an axis.

    Parameters
    ----------
    a : array_like
        Input array.
    axis : int, optional
        By default, the index is into the flattened array, otherwise
        along the specified axis.
    out : ndarray, optional
        If provided, the result will be inserted into this array. It should
        be of the appropriate shape and dtype.
    keepdims : bool, optional
        If this is set to True, the axes which are reduced are left
        in the result as dimensions with size one. With this option,
        the result will broadcast correctly against the array.

    Returns
    -------
    index_array : ndarray[int]
        Array of indices into the array. It has the same shape as `a.shape`
        with the dimension along `axis` removed.

    See Also
    --------
    numpy.argmin

    Notes
    -----
    cuPyNumeric's parallel implementation may yield different results from
    NumPy when the array contains NaN(s).

    Availability
    --------
    Multiple GPUs, Multiple CPUs
    """
    return a.argmin(axis=axis, out=out, keepdims=keepdims)


    Returns the indices of the minimum values along an axis.

    Parameters
    ----------
    a : array_like
        Input array.
    axis : int, optional
        By default, the index is into the flattened array, otherwise
        along the specified axis.
    out : ndarray, optional
        If provided, the result will be inserted into this array. It should
        be of the appropriate shape and dtype.
    keepdims : bool, optional
        If this is set to True, the axes which are reduced are left
        in the result as dimensions with size one. With this option,
        the result will broadcast correctly against the array.

    Returns
    -------
    index_array : ndarray[int]
        Array of indices into the array. It has the same shape as `a.shape`
        with the dimension along `axis` removed.

    See Also
    --------
    numpy.argmin

    Notes
    -----
    cuPyNumeric's parallel implementation may yield different results from
    NumPy when the array contains NaN(s).

    Availability
    --------
    Multiple GPUs, Multiple CPUs
    
cupynumeric/_module/ssc_searching.py