def METHOD_NAME(a_np, w_np, stride, padding, groups=1):
    """Convolution 3D operator in NDHWC layout.
    Parameters
    ----------
    a_np : numpy.ndarray
        5-D with shape [batch, in_channel, in_depth, in_height, in_width]
    w_np : numpy.ndarray
        5-D with shape [num_filter, in_channel, filter_depth, filter_height, filter_width]
    stride : int or a list/tuple of three ints
        Stride size, or [stride_depth, stride_height, stride_width]
    padding : int or str or a list/tuple of three ints
        Padding size, or ['VALID', 'SAME'], or [pad_depth, pad_height, pad_width]
    groups : int
        Number of groups
    Returns
    -------
    b_np : np.ndarray
        5-D with shape [batch, out_channel, out_depth, out_height, out_width]
    """
    a_slices = np.array_split(a_np, groups, axis=4)
    w_slices = np.array_split(w_np, groups, axis=4)
    b_slices = [
        _conv3d_ndhwc_python(a_slice, w_slice, stride, padding)
        for a_slice, w_slice in zip(a_slices, w_slices)
    ]
    b_np = np.concatenate(b_slices, axis=4)
    return b_np