code: def test_regression():
      (x_train, y_train), (x_test, y_test) = boston_housing.load_data()
  
      supervision_metric = "mae"
      ivis_boston = Ivis(
          k=15,
          batch_size=16,
          epochs=2,
          supervision_metric=supervision_metric,
      )
      ivis_boston.fit(x_train, y_train)
  
      embeddings = ivis_boston.transform(x_train)
      y_pred = ivis_boston.score_samples(x_train)
  
      loss_name = ivis_boston.model_.loss["supervised"].__name__
      assert (
          losses.get(loss_name).__name__
          == losses.get(supervision_metric).__name__
      )
      assert (
          ivis_boston.model_.layers[-1].activation.__name__ == "linear"
      )
      assert ivis_boston.model_.layers[-1].output_shape[-1] == 1
  
file_path: ivis/tests/integration/test_supervised.py
imports: import pytest
  from tensorflow.keras.datasets import boston_housing
  from tensorflow.keras.utils import to_categorical
  from tensorflow.keras import losses
  from sklearn import datasets
  import numpy
  from ivis import Ivis
label_desc: Use image node_id 9 for calling a global function with example usage: test_regression() without return types