code: def test_classifier_accuracy(csr_container, fit_intercept, average):
      data = csr_container(X) if csr_container is not None else X
      clf = PassiveAggressiveClassifier(
          C=1.0,
          max_iter=30,
          fit_intercept=fit_intercept,
          random_state=1,
          average=average,
          tol=None,
      )
      clf.fit(data, y)
      score = clf.score(data, y)
      assert score > 0.79
      if average:
          assert hasattr(clf, "_average_coef")
          assert hasattr(clf, "_average_intercept")
          assert hasattr(clf, "_standard_intercept")
          assert hasattr(clf, "_standard_coef")
  
imports: import numpy
  import pytest
  from sklearn.base import ClassifierMixin
  from sklearn.datasets import load_iris
  from sklearn.linear_model import PassiveAggressiveClassifier, PassiveAggressiveRegressor
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 1 for calling a global function with example usage: test_classifier_accuracy(csr_container, fit_intercept, average) without return types