code: def setUp(self):
      # load data
      dataset = load_breast_cancer()
      X = dataset["data"]
      y = dataset["target"]
  
      feature_names = dataset["feature_names"]
  
      xx_train, xx_test, y_train, y_test = train_test_split(
          X, y, test_size=0.1, random_state=42, stratify=y
      )
  
      min_x = np.min(xx_train, axis=0)
      max_x = np.max(xx_train, axis=0)
  
      self.xx_train = (xx_train - min_x[np.newaxis, :]) / (
          max_x[np.newaxis, :] - min_x[np.newaxis, :]
      )
      self.xx_train = pd.DataFrame(self.xx_train, columns=feature_names)
  
      self.xx_test = (xx_test - min_x[np.newaxis, :]) / (
          max_x[np.newaxis, :] - min_x[np.newaxis, :]
      )
      self.xx_test = pd.DataFrame(self.xx_test, columns=feature_names)
  
      self.y_train_onehot = np.concatenate(
          [1 - y_train[:, np.newaxis], y_train[:, np.newaxis]], axis=1
      ).astype("float32")
      self.y_test_onehot = np.concatenate(
          [1 - y_test[:, np.newaxis], y_test[:, np.newaxis]], axis=1
      ).astype("float32")
  
      # train example model
      self.model = RandomForestClassifier(
          n_estimators=2, random_state=10
      )
      self.model.fit(self.xx_train, y_train)
  
file_path: AIX360/tests/nncontrastive/test_nncontrastive.py
imports: import os
  import warnings
  import unittest
  import numpy
  import pandas
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import RandomForestClassifier
  from aix360.algorithms.nncontrastive import NearestNeighborContrastiveExplainer
label_desc: Use image node_id 1 for calling the TestNearestNeighborContrastiveExplainer obj's underlying member method code with example usage: obj.setUp() without return types