class_docstr: Base class for cross validation over a grid of parameters.
  
  The best parameter is saved in alpha_cv
  
  This class is currently not used
class_docstr_tok: ["Base","class","for","cross","validation","over","a","grid","of","parameters",".","The","best","parameter","is","saved","in","alpha_cv","This","class","is","currently","not","used"]
code: def plot_path(self):
      from statsmodels.graphics.utils import _import_mpl
  
      plt = _import_mpl()
      plt.plot(self.alphas, self.cv_error, c="black")
      plt.plot(
          self.alphas, self.cv_error + 1.96 * self.cv_std, c="blue"
      )
      plt.plot(
          self.alphas, self.cv_error - 1.96 * self.cv_std, c="blue"
      )
  
      plt.plot(self.alphas, self.cv_error, "o", c="black")
      plt.plot(
          self.alphas, self.cv_error + 1.96 * self.cv_std, "o", c="blue"
      )
      plt.plot(
          self.alphas, self.cv_error - 1.96 * self.cv_std, "o", c="blue"
      )
  
      return
  
imports: from abc import ABCMeta, abstractmethod
  from statsmodels.compat.python import with_metaclass
  import itertools
  import numpy
  from statsmodels.gam.smooth_basis import GenericSmoothers, UnivariateGenericSmoother
label_desc: Use image node_id 2 for calling the BasePenaltiesPathCV obj's underlying member method code with example usage: obj.plot_path() without return types