code: def test_default_mnist_mlp():
      mnist = tf.keras.datasets.mnist
      epochs = 5
  
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train, x_test = x_train / 255.0, x_test / 255.0
  
      mlp = MultipleLayerPercepton(
          input_shape=(28, 28), num_hidden=[128, 128]
      )
      hist = mlp.fit(x_train, y_train, epochs=epochs)
      mlp.evaluate(x_test, y_test, verbose=2)
      x_test_prob = mlp.predict(x_test)
  
      accuracy = hist.history["accuracy"][0]
      assert_(mlp.accuracy > 0.8)
      assert_(mlp.loss < 0.4)
      assert_equal(mlp.accuracy, accuracy)
      assert_equal(x_test_prob.shape, (10000, 10))
  
imports: import pytest
  from numpy.testing import assert_equal, assert_
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 3 for calling a global function with example usage: test_default_mnist_mlp() without return types