code: def lambda_check_train_targets_fold_start():
      #################### Base Chunk ####################
      initial_target_chunk = TrainTargetChunk(
          pd.DataFrame(
              dict(
                  species=[
                      "setosa",
                      "versicolor",
                      "virginica",
                      "setosa",
                      "versicolor",
                      "virginica",
                      "setosa",
                      "versicolor",
                      "virginica",
                  ]
              )
          )
      )
      initial_target_chunk.T.d = pd.DataFrame(
          dict(species=[0, 1, 2, 0, 1, 2, 0, 1, 2])
      )
  
      #################### Fold 0 Chunk ####################
      fold_0_chunk = deepcopy(initial_target_chunk)
      fold_0_chunk.fold = pd.DataFrame(
          dict(
              species=[
                  "setosa",
                  "setosa",
                  "versicolor",
                  "virginica",
                  "versicolor",
                  "virginica",
              ]
          ),
          index=[0, 3, 4, 5, 7, 8],  # OOF: 1, 2, 6
      )
      fold_0_chunk.T.fold = pd.DataFrame(
          dict(species=[0, 0, 1, 2, 1, 2]), index=[0, 3, 4, 5, 7, 8]
      )
  
      #################### Fold 1 Chunk ####################
      fold_1_chunk = deepcopy(initial_target_chunk)
      fold_1_chunk.fold = pd.DataFrame(
          dict(
              species=[
                  "versicolor",
                  "virginica",
                  "setosa",
                  "virginica",
                  "setosa",
                  "versicolor",
              ]
          ),
          index=[1, 2, 3, 5, 6, 7],  # OOF: 0, 4, 8
      )
      fold_1_chunk.T.fold = pd.DataFrame(
          dict(species=[1, 2, 0, 2, 0, 1]), index=[1, 2, 3, 5, 6, 7]
      )
  
      #################### Fold 2 Chunk ####################
      fold_2_chunk = deepcopy(initial_target_chunk)
      fold_2_chunk.fold = pd.DataFrame(
          dict(
              species=[
                  "setosa",
                  "versicolor",
                  "virginica",
                  "versicolor",
                  "setosa",
                  "virginica",
              ]
          ),
          index=[0, 1, 2, 4, 6, 8],  # OOF: 3, 5, 7
      )
      fold_2_chunk.T.fold = pd.DataFrame(
          dict(species=[0, 1, 2, 1, 0, 2]), index=[0, 1, 2, 4, 6, 8]
      )
  
      return lambda_check_train_targets(
          on_fold_start=[fold_0_chunk, fold_1_chunk, fold_2_chunk]
      )
  
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 8 for calling a global function with example usage: lambda_check_train_targets_fold_start() and returns: lambda_check_train_targets