code: def get_iris_sample():
      """Manageable subset of Iris classification dataset, comprising three samples for each of the
      three classes - nine rows in all"""
      sample_df = pd.DataFrame(
          data=[
              [5.1, 3.5, 1.4, 0.2, "setosa"],  # 0
              [7.0, 3.2, 4.7, 1.4, "versicolor"],  # 1
              [6.3, 3.3, 6.0, 2.5, "virginica"],  # 2
              [4.9, 3.0, 1.4, 0.2, "setosa"],  # 3
              [6.4, 3.2, 4.5, 1.5, "versicolor"],  # 4
              [5.8, 2.7, 5.1, 1.9, "virginica"],  # 5
              [4.7, 3.2, 1.3, 0.2, "setosa"],  # 6
              [6.9, 3.1, 4.9, 1.5, "versicolor"],  # 7
              [7.1, 3.0, 5.9, 2.1, "virginica"],  # 8
          ],
          columns=[
              "sepal_length",
              "sepal_width",
              "petal_length",
              "petal_width",
              "species",
          ],
      )
      return sample_df
  
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 1 for calling a global function with example usage: get_iris_sample() and returns: sample_df