code: def test_find_label_issues_using_argmax_confusion_matrix(
      calibrate, filter_by
  ):
      label_issues = (
          filter.find_label_issues_using_argmax_confusion_matrix(
              labels_,
              pred_probs_,
              calibrate=calibrate,
              filter_by=filter_by,
          )
      )
      assert all(
          label_issues
          == np.array(
              [
                  False,
                  False,
                  True,
                  False,
                  False,
                  False,
                  False,
                  False,
                  False,
                  False,
              ]
          )
      )
  
imports: import cleanlab.multilabel_classification.dataset
  from cleanlab import count, filter
  from cleanlab.count import get_confident_thresholds, estimate_py_and_noise_matrices_from_probabilities
  from cleanlab.internal.latent_algebra import compute_inv_noise_matrix
  from cleanlab.benchmarking.noise_generation import generate_noise_matrix_from_trace
  from cleanlab.benchmarking.noise_generation import generate_noisy_labels
  from cleanlab.internal.util import value_counts
  from cleanlab.internal.multilabel_utils import int2onehot
  from cleanlab.experimental.label_issues_batched import find_label_issues_batched
  import numpy
  import scipy
  import pytest
  from sklearn.multioutput import MultiOutputClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import cross_val_predict
  from tempfile import mkdtemp
  import os.path
label_desc: Use image node_id 25 for calling a global function with example usage: test_find_label_issues_using_argmax_confusion_matrix(calibrate, filter_by) without return types