code: def test_fit_pred_probs(format):
      data = DATA_FORMATS[format]
  
      cl = CleanLearning()
      pred_probs = estimate_cv_predicted_probabilities(
          X=data["X_train"],
          labels=data["true_labels_train"],
      )
      cl.fit(
          X=data["X_train"],
          labels=data["true_labels_train"],
          pred_probs=pred_probs,
      )
      score_with_pred_probs = cl.score(
          data["X_test"], data["true_labels_test"]
      )
      cl = CleanLearning()
      cl.fit(
          X=data["X_train"],
          labels=data["true_labels_train"],
      )
      score_no_pred_probs = cl.score(
          data["X_test"], data["true_labels_test"]
      )
      assert abs(score_with_pred_probs - score_no_pred_probs) < 0.01
  
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 26 for calling a global function with example usage: test_fit_pred_probs(format) without return types