code: def test_pred_and_pred_proba(format):
      data = DATA_FORMATS[format]
      cl = CleanLearning()
      cl.fit(data["X_train"], data["labels"])
      n = np.shape(data["true_labels_test"])[0]
      m = len(np.unique(data["true_labels_test"]))
      pred = cl.predict(data["X_test"])
      probs = cl.predict_proba(data["X_test"])
      # Just check that this functions return what we expect
      assert np.shape(pred)[0] == n
      assert np.shape(probs) == (n, m)
  
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 22 for calling a global function with example usage: test_pred_and_pred_proba(format) without return types