code: def test_classification_report_imbalanced_dict_without_target_names():
      iris = datasets.load_iris()
      y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)
      print(iris.target_names)
      report = classification_report_imbalanced(
          y_true,
          y_pred,
          labels=np.arange(len(iris.target_names)),
          output_dict=True,
      )
      print(report.keys())
      outer_keys = set(report.keys())
      inner_keys = set(report["0"].keys())
  
      expected_outer_keys = {
          "0",
          "1",
          "2",
          "avg_pre",
          "avg_rec",
          "avg_spe",
          "avg_f1",
          "avg_geo",
          "avg_iba",
          "total_support",
      }
      expected_inner_keys = {
          "spe",
          "f1",
          "sup",
          "rec",
          "geo",
          "iba",
          "pre",
      }
  
      assert outer_keys == expected_outer_keys
      assert inner_keys == expected_inner_keys
  
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 24 for calling a global function with example usage: test_classification_report_imbalanced_dict_without_target_names() without return types