code: def setUp(self):
      xor_x_train = np.array([[-1, -1], [-1, 1], [1, -1], [1, 1]])
      xor_y_train = np.array([[1, -1, -1, 1]]).T
  
      self.data = np.concatenate(
          [
              xor_x_train,
              xor_x_train + 0.1,
              xor_x_train + np.array([[0.1, -0.1]]),
              xor_x_train - 0.1,
              xor_x_train - np.array([[0.1, -0.1]]),
          ],
          axis=0,
      )
      target = np.concatenate(
          [
              xor_y_train,
              xor_y_train,
              xor_y_train,
              xor_y_train,
              xor_y_train,
          ],
          axis=0,
      )
      self.target = np.where(target == -1, 0, target)
      super(LVQTestCase, self).setUp()
  
imports: from functools import partial
  import numpy
  from sklearn import datasets
  from neupy import algorithms, init
  from neupy.exceptions import NotTrained
  from base import BaseTestCase
  from helpers import compare_networks
label_desc: Use image node_id 1 for calling the LVQTestCase obj's underlying member method code with example usage: obj.setUp() without return types