code: def test_no_fit_sample_weight(format):
      data = DATA_FORMATS[format]
  
      class Struct:
          def fit(self, X, y):
              pass
  
          def predict_proba(self):
              pass
  
          def predict(self, X):
              return data["true_labels_test"]
  
      n = np.shape(data["true_labels_test"])[0]
      m = len(np.unique(data["true_labels_test"]))
      pred_probs = np.zeros(shape=(n, m))
      cl = CleanLearning(clf=Struct())
      cl.fit(
          data["X_train"],
          data["true_labels_train"],
          pred_probs=pred_probs,
          noise_matrix=data["noise_matrix"],
      )
  
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 25 for calling a global function with example usage: test_no_fit_sample_weight(format) and returns: data