code: def test_dataframe_accuracy_score(setup):
      rs = np.random.RandomState(0)
      raw = pd.DataFrame(
          {"a": rs.randint(0, 10, (10,)), "b": rs.randint(0, 10, (10,))}
      )
  
      df = md.DataFrame(raw)
      y = df["a"].to_tensor().astype("int")
      pred = df["b"].astype("int")
  
      score = accuracy_score(y, pred)
      expect = sklearn_accuracy_score(
          raw["a"].to_numpy().astype("int"),
          raw["b"].to_numpy().astype("int"),
      )
      assert pytest.approx(score.fetch()) == expect
  
imports: import re
  import warnings
  import numpy
  import pandas
  import pytest
  from sklearn.metrics import roc_curve, auc, accuracy_score
  from sklearn.metrics.tests.test_ranking import make_prediction, _auc
  from sklearn.exceptions import UndefinedMetricWarning
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal
  from ....None import dataframe
  from ....None import tensor
  from ...utils.extmath import softmax
  from ..None import roc_curve, auc, accuracy_score, roc_auc_score
label_desc: Use image node_id 22 for calling a global function with example usage: test_dataframe_accuracy_score(setup) without return types