code: def test_no_score(format):
      data = DATA_FORMATS[format]
  
      class Struct:
          def fit(self):
              pass
  
          def predict_proba(self):
              pass
  
          def predict(self, X):
              return data["true_labels_test"]
  
      cl = CleanLearning(clf=Struct())
      score = cl.score(data["X_test"], data["true_labels_test"])
      assert abs(score - 1) < 1e-6
  
imports: from copy import deepcopy
  import warnings
  from sklearn.linear_model import LogisticRegression
  from sklearn.base import BaseEstimator
  from sklearn.model_selection import GridSearchCV
  import scipy
  import pytest
  import numpy
  import pandas
  from cleanlab.classification import CleanLearning
  from cleanlab.benchmarking.noise_generation import generate_noise_matrix_from_trace
  from cleanlab.benchmarking.noise_generation import generate_noisy_labels
  from cleanlab.internal.latent_algebra import compute_inv_noise_matrix
  from cleanlab.count import compute_confident_joint, estimate_cv_predicted_probabilities, get_confident_thresholds
  from cleanlab.filter import find_label_issues
label_desc: Use image node_id 24 for calling a global function with example usage: test_no_score(format) and returns: data