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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 get_contour(mask):
 """Compute the image contour from a mask The contour is computed in a very inefficient way using scikit-image and a conversion of float coordinates to pixel coordinates. Parameters mask: binary ndarray of shape (M,N) or (K,M,N) The mask outlining the pixel positions of the event. If a 3d array is given, then `K` indexes the individual contours. Returns ------- cont: ndarray or list of K ndarrays of shape (J,2) A 2D array that holds the contour of an event (in pixels) e.g. obtained using `mm.contour` where `mm` is an instance of `RTDCBase`. The first and second columns of `cont` correspond to the x- and y-coordinates of the contour. """

    if isinstance(mask, np.ndarray) and len(mask.shape) == 2:
        mask = [mask]
        ret_list = False
    else:
        ret_list = True
    contours = []

    for mi in mask:
        c0 = find_contours(mi.transpose(),
                           level=.9999,
                           positive_orientation="low",
                           fully_connected="high")[0]
        # round all coordinates to pixel values
        c1 = np.asarray(np.round(c0), int)
        # remove duplicates
        c2 = remove_duplicates(c1)
        contours.append(c2)
    if ret_list:
        return contours
    else:
        return contours[0]