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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 calibrate_dates(chron, calib_curve, d_r, d_std, cutoff=0.0001, normal_distr=False, t_a=[3], t_b=[4]):
 """Get density of calendar dates for chron date segment in core Parameters chron : DatedProxy-like calib_curve : CalibCurve or list of CalibCurves d_r : scalar or ndarray Carbon reservoir offset. d_std : scalar or ndarray Carbon reservoir offset error standard deviation. cutoff : scalar, optional Unknown. normal_distr : Bool, optional Use normal distribution for date errors. If False, then use Student's t-distribution. t_a : scalar or ndarray, optional Student's t-distribution parameter, a. t_a - 1 must equal t_b. t_b : scalar or ndarray, optional Student's t-distribution parameter, b. t_a - 1 must equal t_b. Returns ------- depth : ndarray Depth of dated sediment sample. probs : list of 2d arrays Density of calendar age for each dated sediment sample. For each sediment sample, the 2d array has two columns, the first is the calendar age. The second column is the density for that calendar age. """

    # Python version of .bacon.calib() on line 908 in Bacon.R

    # .bacon.calib - line 908

    # rcmean = 4128; w2 = 4225; t_a=3; t_b=4
    # test = d_cal(cc = calib_curve.rename(columns = {0:'a', 1:'b', 2:'c'}), rcmean = 4128, w2 = 4225, t_a=t_a,
    # t_b=t_b, cutoff=cutoff, normal = normal)

    # Line 959 of Bacon.R
    # calib = list(dets.iloc[:, 3])
    # Now Bacon goes and checks the ncol in the dets See line #960 in Bacon.R

    # Line #973
    # TODO(brews): Check that `normal_dist` is used and documented correctly in docstring above.
    # TODO(brews): Check whether we call returned values densities, freqs or what options we should have.
    n = len(chron.depth)
    calib_curve = np.array(calib_curve)
    t_a = np.array(t_a)
    t_b = np.array(t_b)
    assert t_b - 1 == t_a
    d_r = np.array(d_r)
    d_std = np.array(d_std)
    if len(t_a) == 1:
        t_a = np.repeat(t_a, n)
    if len(t_b) == 1:
        t_b = np.repeat(t_b, n)
    if len(d_r) == 1:
        d_r = np.repeat(d_r, n)
    if len(d_std) == 1:
        d_std = np.repeat(d_std, n)
    if len(calib_curve) == 1:
        calib_curve = np.repeat(calib_curve, n)

    calib_probs = []
    rcmean = chron.age - d_r
    w2 = chron.error ** 2 + d_std ** 2
    for i in range(n):
        age_realizations = d_cal(calib_curve[i], rcmean=rcmean[i], w2=w2[i],
                                 t_a=t_a[i], t_b=t_b[i],
                                 cutoff=cutoff, normal_distr=normal_distr)
        calib_probs.append(age_realizations)
    return np.array(chron.depth), calib_probs