code: def test_find_label_issues_uses_thresholds():
      X = DATA["X_train"]
      labels = DATA["labels"]
      pred_probs = estimate_cv_predicted_probabilities(
          X=X, labels=labels
      )
  
      confident_thresholds = get_confident_thresholds(
          labels=labels, pred_probs=pred_probs
      )
      confident_joint = compute_confident_joint(
          labels=labels, pred_probs=pred_probs
      )
  
      # regular find label issues with no args
      cl = CleanLearning()
      label_issues_reg = cl.find_label_issues(
          labels=labels, pred_probs=pred_probs
      )
  
      # find label issues with specified confident thresholds
      cl = CleanLearning()
      label_issues_thres = cl.find_label_issues(
          labels=labels,
          pred_probs=pred_probs,
          thresholds=confident_thresholds,
      )
  
      # find label issues with specified confident joint
      cl = CleanLearning(
          find_label_issues_kwargs={
              "confident_joint": confident_joint,
          }
      )
      label_issues_cj = cl.find_label_issues(
          labels=labels, pred_probs=pred_probs
      )
  
      # the labels issues in above three calls should be the same
      assert np.sum(label_issues_reg["is_label_issue"]) == np.sum(
          label_issues_thres["is_label_issue"]
      )
      assert np.sum(label_issues_reg["is_label_issue"]) == np.sum(
          label_issues_cj["is_label_issue"]
      )
  
      # find label issues with different specified confident thresholds
      confident_thresholds_alt = np.full(pred_probs.shape[1], 0.25)
      cl = CleanLearning()
      label_issues_thres_alt = cl.find_label_issues(
          labels=labels,
          pred_probs=pred_probs,
          thresholds=confident_thresholds_alt,
      )
  
      # find label issues with different specified confident joint
      confident_joint_alt = compute_confident_joint(
          labels=labels,
          pred_probs=pred_probs,
          thresholds=confident_thresholds_alt,
      )
      cl = CleanLearning(
          find_label_issues_kwargs={
              "confident_joint": confident_joint_alt,
          }
      )
      label_issues_cj_alt = cl.find_label_issues(
          labels=labels, pred_probs=pred_probs
      )
  
      # the number of issues for these 2 alt calls should be same as one another, but different from above 3
      assert np.sum(label_issues_thres_alt["is_label_issue"]) == np.sum(
          label_issues_cj_alt["is_label_issue"]
      )
      assert np.sum(label_issues_thres_alt["is_label_issue"]) != np.sum(
          label_issues_reg["is_label_issue"]
      )
  
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 33 for calling a global function with example usage: test_find_label_issues_uses_thresholds() without return types