code: def mini_iris_env(request):
      """`Environment` fixture that supports provision of a `holdout_dataset`/`test_dataset` `request`
  
      Parameters
      ----------
      request: Object
          If `request` has a "param" attribute, it must be a dict, containing any, or none of the
          keys {"holdout", "test", "callbacks"}, whose values will be passed as the `Environment`'s
          `holdout_dataset`, `test_dataset` and `experiment_callbacks` parameters, respectively
      """
      params = getattr(request, "param", dict())
      return Environment(
          train_dataset=get_iris_sample(),
          holdout_dataset=params.get("holdout", None),
          test_dataset=params.get("test", None),
          results_path=assets_dir,
          metrics=dict(f1=lambda t, p: f1_score(t, p, average="micro")),
          target_column="species",
          cv_params=dict(n_splits=3, shuffle=True, random_state=32),
          experiment_callbacks=params.get("callbacks", []),
      )
  
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 4 for calling a global function with example usage: mini_iris_env(request) and returns: Environment