code: def test_default_mnist_sequential():
      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
  
      model = tf.keras.models.Sequential(
          [
              tf.keras.layers.Flatten(input_shape=(28, 28)),
              tf.keras.layers.Dense(128, activation="relu"),
              tf.keras.layers.Dropout(0.2),
              tf.keras.layers.Dense(10, activation="softmax"),
          ]
      )
  
      model.compile(
          optimizer="adam",
          loss="sparse_categorical_crossentropy",
          metrics=["accuracy"],
      )
  
      hist = model.fit(x_train, y_train, epochs=epochs)
      model.evaluate(x_test, y_test, verbose=2)
      accuracy = hist.history["accuracy"][0]
      assert_(accuracy > 0.9)
  
imports: import pytest
  from numpy.testing import assert_equal, assert_
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 1 for calling a global function with example usage: test_default_mnist_sequential() without return types