code: def label_encode_targets(all_targets):
      label_encoder = LabelEncoder()
      all_targets.loc[:, "species"] = label_encoder.fit_transform(
          all_targets.loc[:, "species"]
      )
      # return all_targets, label_encoder  # TODO: Test support
      return all_targets
  
file_path: hyperparameter_hunter/tests/integration_tests/test_data_wranglers.py
imports: from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer
  from hyperparameter_hunter.callbacks.recipes import lambda_check_train_targets
  from hyperparameter_hunter.data.data_chunks.target_chunks import TrainTargetChunk
  from copy import deepcopy
  import pandas
  import pytest
  from sklearn.metrics import f1_score
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import LabelEncoder
  from sklearn.svm import SVC
label_desc: Use image node_id 3 for calling a global function with example usage: label_encode_targets(all_targets) and returns: all_targets