code: def test_score_tbp(
      self, monkeypatch, weak_labels, tbp, vrb, expected
  ):
      def mock_predict(weak_label_matrix, verbose):
          assert verbose is vrb
          assert len(weak_label_matrix) == 3
          return np.array(
              [
                  [0.8, 0.1, 0.1],
                  [0.4, 0.4, 0.2],
                  [1 / 3.0, 1 / 3.0, 1 / 3.0],
              ]
          )
  
      label_model = FlyingSquid(weak_labels)
  
      monkeypatch.setattr(label_model, "_predict", mock_predict)
  
      metrics = label_model.score(tie_break_policy=tbp, verbose=vrb)
  
      assert metrics["accuracy"] == pytest.approx(expected)
      if tbp == "abstain":
          assert list(metrics.keys())[:1] == ["negative"]
      elif tbp == "random":
          assert list(metrics.keys())[:3] == [
              "negative",
              "positive",
              "neutral",
          ]
  
imports: import sys
  import numpy
  import pytest
  from argilla.client.models import TextClassificationRecord
  from argilla.labeling.text_classification import FlyingSquid, Snorkel, WeakLabels, WeakMultiLabels
  from argilla.labeling.text_classification.label_models import LabelModel, MajorityVoter, MissingAnnotationError, NotFittedError, TieBreakPolicy, TooFewRulesError
label_desc: Use image node_id 12 for calling the TestFlyingSquid obj's underlying member method code with example usage: obj.test_score_tbp(monkeypatch, weak_labels, tbp, vrb, expected) and returns: np