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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 tvd(x0, rho, gamma):
 """ Proximal operator for the total variation denoising penalty Requires scikit-image be installed Parameters x0 : array_like The starting or initial point used in the proximal update step rho : float Momentum parameter for the proximal step (larger value -> stays closer to x0) gamma : float A constant that weights how strongly to enforce the constraint Returns ------- theta : array_like The parameter vector found after running the proximal update step Raises ------ ImportError If scikit-image fails to be imported """

    try:
        from skimage.restoration import denoise_tv_bregman
    except ImportError:
        print('Error: scikit-image not found. TVD will not work.')
        return x0

    return denoise_tv_bregman(x0, rho / gamma)