code: def generate_hazard_rates(
      n,
      d,
      timelines,
      constant=False,
      independent=0,
      n_binary=0,
      model="aalen",
  ):
      """
        n: the number of instances
        d: the number of covariates
        lifelines: the observational times
        constant: make the coefficients constant (not time dependent)
        n_binary: the number of binary covariates
        model: from ["aalen", "cox"]
  
      Returns:s
        hazard rates: (t,n) dataframe,
        coefficients: (t,d+1) dataframe of coefficients,
        covarites: (n,d) dataframe
  
      """
      covariates = generate_covariates(n, d, n_binary=n_binary)
      if model == "aalen":
          coefficients = time_varying_coefficients(
              d + 1,
              timelines,
              independent=independent,
              constant=constant,
          )
          hazard_rates = np.dot(covariates, coefficients.T)
          return (
              pd.DataFrame(hazard_rates.T, index=timelines),
              coefficients,
              pd.DataFrame(covariates),
          )
      if model == "cox":
          covariates = covariates[:, :-1]
          coefficients = constant_coefficients(
              d, timelines, independent
          )
          baseline = time_varying_coefficients(1, timelines)
          hazard_rates = (
              np.exp(np.dot(covariates, coefficients.T))
              * baseline[baseline.columns[0]].values
          )
          coefficients[
              "baseline: " + baseline.columns[0]
          ] = baseline.values
          return (
              pd.DataFrame(hazard_rates.T, index=timelines),
              coefficients,
              pd.DataFrame(covariates),
          )
      raise Exception
  
imports: import numpy
  import pandas
  from scipy import stats
  from scipy.optimize import newton
  from scipy.integrate import cumtrapz
label_desc: Use image node_id 12 for calling a global function with example usage: generate_hazard_rates(n, d, timelines, constant, independent, n_binary, model) and returns: pd,  coefficients,  pd, pd,  coefficients,  pd