code: def test_fit(
      art_warning, get_default_mnist_subset, default_batch_size
  ):
      try:
          (x_train_mnist, y_train_mnist), (
              x_test_mnist,
              y_test_mnist,
          ) = get_default_mnist_subset
  
          labels = np.argmax(y_test_mnist, axis=1)
  
          accuracy = (
              np.sum(
                  np.argmax(classifier.predict(x_test_mnist), axis=1)
                  == labels
              )
              / x_test_mnist.shape[0]
          )
          np.testing.assert_array_almost_equal(
              accuracy, 0.32, decimal=0.06
          )
  
          classifier.fit(
              x_train_mnist,
              y_train_mnist,
              batch_size=default_batch_size,
              nb_epochs=2,
          )
          accuracy_2 = (
              np.sum(
                  np.argmax(classifier.predict(x_test_mnist), axis=1)
                  == labels
              )
              / x_test_mnist.shape[0]
          )
          np.testing.assert_array_almost_equal(
              accuracy_2, 0.73, decimal=0.06
          )
  
      except ARTTestException as e:
          art_warning(e)
  
file_path: adversarial-robustness-toolbox/tests/estimators/classification/test_jax.py
imports: import logging
  import numpy
  import pytest
  from jax import random
  from jax import grad, jit, vmap
  import jax.numpy
  from jax.scipy.special import logsumexp
  from art.experimental.estimators.classification.jax import JaxClassifier
  from tests.utils import ARTTestException
label_desc: Use image node_id 2 for calling a global function with example usage: test_fit(art_warning, get_default_mnist_subset, default_batch_size) without return types