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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 compute_jackson_cheby_coeff(filter_bounds, delta_lambda, m):
 r""" To compute the m+1 coefficients of the polynomial approximation of an ideal band-pass between a and b, between a range of values defined by lambda_min and lambda_max. Parameters filter_bounds : list [a, b] delta_lambda : list [lambda_min, lambda_max] m : int Returns ------- ch : ndarray jch : ndarray References :cite:`tremblay2016compressive` """

    # Parameters check
    if delta_lambda[0] > filter_bounds[0] or delta_lambda[1] < filter_bounds[1]:
        _logger.error("Bounds of the filter are out of the lambda values")
        raise()
    elif delta_lambda[0] > delta_lambda[1]:
        _logger.error("lambda_min is greater than lambda_max")
        raise()

    # Scaling and translating to standard cheby interval
    a1 = (delta_lambda[1]-delta_lambda[0])/2
    a2 = (delta_lambda[1]+delta_lambda[0])/2

    # Scaling bounds of the band pass according to lrange
    filter_bounds[0] = (filter_bounds[0]-a2)/a1
    filter_bounds[1] = (filter_bounds[1]-a2)/a1

    # First compute cheby coeffs
    ch = np.empty(m+1, dtype=float)
    ch[0] = (2/(np.pi))*(np.arccos(filter_bounds[0])-np.arccos(filter_bounds[1]))
    for i in range(1, len(ch)):
        ch[i] = (2/(np.pi * i)) * \
            (np.sin(i * np.arccos(filter_bounds[0])) - np.sin(i * np.arccos(filter_bounds[1])))

    # Then compute jackson coeffs
    jch = np.empty(m+1, dtype=float)
    alpha = (np.pi/(m+2))
    for i in range(len(jch)):
        jch[i] = (1/np.sin(alpha)) * \
            ((1 - i/(m+2)) * np.sin(alpha) * np.cos(i * alpha) +
             (1/(m+2)) * np.cos(alpha) * np.sin(i * alpha))

    # Combine jackson and cheby coeffs
    jch = ch * jch

    return ch, jch