code: def test_classification_report_imbalanced_multiclass_with_long_string_label():
      y_true, y_pred, _ = make_prediction(binary=False)
  
      labels = np.array(["blue", "green" * 5, "red"])
      y_true = labels[y_true]
      y_pred = labels[y_pred]
  
      expected_report = (
          "pre rec spe f1 geo iba sup blue 0.83 0.79 0.92 0.81 "
          "0.85 0.72 24 greengreengreengreengreen 0.33 0.10 "
          "0.86 0.15 0.29 0.08 31 red 0.42 0.90 0.55 0.57 0.70 "
          "0.51 20 avg / total 0.51 0.53 0.80 0.47 0.58 0.40 75"
      )
  
      report = classification_report_imbalanced(y_true, y_pred)
      assert _format_report(report) == expected_report
  
imports: from functools import partial
  import numpy
  import pytest
  from sklearn import datasets, svm
  from sklearn.metrics import accuracy_score, average_precision_score, brier_score_loss, cohen_kappa_score, jaccard_score, precision_score, recall_score, roc_auc_score
  from sklearn.preprocessing import label_binarize
  from sklearn.utils._testing import assert_allclose, assert_array_equal, assert_no_warnings
  from sklearn.utils.validation import check_random_state
  from imblearn.metrics import classification_report_imbalanced, geometric_mean_score, macro_averaged_mean_absolute_error, make_index_balanced_accuracy, sensitivity_score, sensitivity_specificity_support, specificity_score
label_desc: Use image node_id 20 for calling a global function with example usage: test_classification_report_imbalanced_multiclass_with_long_string_label() without return types