code: def plot_learning_curves(self, hyperparameter):
      if hyperparameter == "TV":
          C = self.C_tv_history
      elif hyperparameter == "Group L1":
          C = self.C_group_l1_history
      else:
          raise ValueError(
              "hyperparameter value should be either `TV` or"
              " `Group L1`"
          )
      x = np.log10(C)
      order = np.argsort(x)
      m = np.array(self.kfold_mean_train_scores)[order]
      sd = np.array(self.kfold_sd_train_scores)[order]
      fig = plt.figure()
      ax = plt.gca()
      p1 = ax.plot(x[order], m)
      p2 = ax.fill_between(x[order], m - sd, m + sd, alpha=0.3)
      min_point_train = np.min(m - sd)
      m = np.array(self.kfold_mean_test_scores)[order]
      sd = np.array(self.kfold_sd_test_scores)[order]
      p3 = ax.plot(x[order], m)
      p4 = ax.fill_between(x[order], m - sd, m + sd, alpha=0.3)
      min_point_test = np.min(m - sd)
      min_point = min(min_point_train, min_point_test)
      p5 = plt.scatter(np.log10(C), min_point * np.ones_like(C))
  
      ax.legend(
          [(p1[0], p2), (p3[0], p4), p5],
          ["train score", "test score", "tested hyperparameters"],
          loc="lower right",
      )
      ax.set_title("Learning curves")
      ax.set_xlabel("C %s (log scale)" % hyperparameter)
      ax.set_ylabel("Loss")
      return fig, ax
  
imports: import inspect
  import numpy
  from abc import ABC
  from operator import itemgetter
  from collections import namedtuple
  from sklearn.model_selection import StratifiedKFold
  from tick.base import Base
  from tick.prox import ProxZero, ProxMulti, ProxTV, ProxEquality, ProxGroupL1
  from tick.solver import SVRG
  from tick.survival import SimuSCCS, ModelSCCS
  from tick.preprocessing import LongitudinalSamplesFilter, LongitudinalFeaturesLagger
  from scipy.stats import uniform
  import matplotlib.pylab
  from mpl_toolkits.mplot3d import Axes3D
  from matplotlib import cm
  from tick.preprocessing.utils import check_longitudinal_features_consistency, check_censoring_consistency, safe_array
label_desc: Use image node_id 7 for calling the CrossValidationTracker obj's underlying member method code with example usage: obj.plot_learning_curves(hyperparameter) and returns: fig,  ax