class_docstr: Test the LearningCurve visualizer
docstr: Test learning curve with bad input for training size.
docstr_tok: ["Test","learning","curve","with","bad","input","for","training","size","."]
code: def test_bad_train_sizes(self):
      """
      Test learning curve with bad input for training size.
      """
      with pytest.raises(YellowbrickValueError):
          LearningCurve(LinearSVC(), train_sizes=10000)
  
imports: import sys
  import pytest
  import numpy
  from unittest.mock import patch
  from tests.base import VisualTestCase
  from sklearn.svm import LinearSVC
  from sklearn.linear_model import Ridge
  from sklearn.naive_bayes import GaussianNB, MultinomialNB
  from sklearn.cluster import MiniBatchKMeans
  from sklearn.preprocessing import OneHotEncoder, LabelEncoder
  from sklearn.impute import SimpleImputer
  from sklearn.model_selection import ShuffleSplit
  from sklearn.model_selection import StratifiedKFold
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from yellowbrick.datasets import load_mushroom, load_game
  from yellowbrick.model_selection.learning_curve import *
label_desc: Use image node_id 9 for calling the TestLearningCurve obj's underlying member method code with example usage: obj.test_bad_train_sizes() without return types