code: def test_sklearn_accuracy_score(
      arrays_and_dtypes,
      on_device,
      fn_tree,
      frontend,
      test_flags,
      backend_fw,
      normalize,
  ):
      dtypes, values = arrays_and_dtypes
      # sklearn accuracy_score does not support continuous values
      for i in range(2):
          if "float" in dtypes[i]:
              values[i] = np.floor(values[i])
      helpers.test_frontend_function(
          input_dtypes=dtypes,
          backend_to_test=backend_fw,
          test_flags=test_flags,
          fn_tree=fn_tree,
          frontend=frontend,
          on_device=on_device,
          y_true=values[0],
          y_pred=values[1],
          normalize=normalize,
          sample_weight=None,
      )
  
imports: from hypothesis import strategies
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: test_sklearn_accuracy_score(arrays_and_dtypes, on_device, fn_tree, frontend, test_flags, backend_fw, normalize) without return types