code: def test_pruning_reduce_prune_counts():
      """Make sure it doesnt remove when its not supposed to"""
      cj = np.array(
          [
              [325, 16, 22],
              [47, 178, 10],
              [36, 8, 159],
          ]
      )
      cj2 = filter._reduce_prune_counts(cj, frac_noise=1.0)
      assert np.all(cj == cj2)
  
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 16 for calling a global function with example usage: test_pruning_reduce_prune_counts() without return types