code: def test_clf_fit_nm_inm(format):
      data = DATA_FORMATS[format]
      cl = CleanLearning(seed=SEED)
      nm = data["noise_matrix"]
      inm = compute_inv_noise_matrix(
          py=data["py"],
          noise_matrix=nm,
          ps=data["ps"],
      )
      cl.fit(
          X=data["X_train"],
          labels=data["labels"],
          noise_matrix=nm,
          inverse_noise_matrix=inm,
      )
      score_nm_inm = cl.score(data["X_test"], data["true_labels_test"])
  
      # Learn with noisy labels and estimate the inv noise matrix.
      cl2 = CleanLearning(seed=SEED)
      cl2.fit(
          data["X_train"],
          data["labels"],
      )
      score = cl2.score(data["X_test"], data["true_labels_test"])
      assert score < score_nm_inm + 1e-4
  
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 20 for calling a global function with example usage: test_clf_fit_nm_inm(format) without return types