code: def test_target_explained_variance(self):
      np.random.seed(0)
      clf = PCA()
      clf.fit(self.X)
      ax = skplt.plot_pca_component_variance(
          clf, target_explained_variance=0
      )
      ax = skplt.plot_pca_component_variance(
          clf, target_explained_variance=0.5
      )
      ax = skplt.plot_pca_component_variance(
          clf, target_explained_variance=1
      )
      ax = skplt.plot_pca_component_variance(
          clf, target_explained_variance=1.5
      )
  
imports: from __future__ import absolute_import
  import unittest
  import scikitplot.plotters
  from sklearn.datasets import load_iris
  from sklearn.decomposition import PCA
  import numpy
  import matplotlib.pyplot
label_desc: Use image node_id 3 for calling the TestPlotPCAComponentVariance obj's underlying member method code with example usage: obj.test_target_explained_variance() without return types