code: def get_iris_data(target="species"):
      data = load_iris()
      df = pd.DataFrame(
          data=data.data,
          columns=[_.replace(" ", "_") for _ in data.feature_names],
          index=[
              f"s_{str(_).rjust(3, '_')}_s"
              for _ in range(len(data.data))
          ],
      )
      df[target] = data.target
      #################### Label-Encode Targets ####################
      label_encoder = LabelEncoder()
      df.loc[:, target] = label_encoder.fit_transform(df.loc[:, target])
      return df
  
imports: from hyperparameter_hunter import Environment, CVExperiment
  from hyperparameter_hunter.callbacks.bases import BaseCallback
  from hyperparameter_hunter.settings import G
  from hyperparameter_hunter.utils.learning_utils import get_breast_cancer_data
  from copy import deepcopy
  import pandas
  import pytest
  from sklearn.datasets import load_iris
  from sklearn.metrics import f1_score
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import LabelEncoder
  from xgboost import XGBClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: get_iris_data(target) and returns: df