class_docstr: Proportional Hazard model
  
  References
  ------------
  Royston, P., & Parmar, M. K. B. (2002). Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine, 21(15), 2175–2197. doi:10.1002/sim.1203
code: def _cumulative_hazard(self, params, T, Xs):
      exp_Xbeta = np.exp(np.dot(Xs["beta_"], params["beta_"]))
      lT = np.log(T)
      return exp_Xbeta * np.exp(
          params["phi1_"] * lT
          + params["phi2_"]
          * self.basis(
              lT,
              np.log(self.KNOTS[1]),
              np.log(self.KNOTS[0]),
              np.log(self.KNOTS[-1]),
          )
      )
  
imports: from autograd import numpy
  from lifelines.datasets import load_lymph_node
  from lifelines.fitters import ParametricRegressionFitter
  from autograd.scipy.special import expit
  import pandas
  from lifelines import CoxPHFitter
  from matplotlib import pyplot
label_desc: Use image node_id 2 for calling the PHSplineFitter obj's underlying member method code with example usage: obj._cumulative_hazard(params, T, Xs) and returns: unknown