code: def evaluation_inputs(request, tmp_recipe_exec_path):
      split_step_output_dir = tmp_recipe_exec_path.joinpath(
          "steps", "split", "outputs"
      )
      split_step_output_dir.mkdir(parents=True)
      if "classification" in request.keywords:
          X, y = load_iris(as_frame=True, return_X_y=True)
      else:
          X, y = load_diabetes(as_frame=True, return_X_y=True)
      validation_df = X.assign(y=y).sample(n=50, random_state=9)
      validation_df.to_parquet(
          split_step_output_dir.joinpath(_OUTPUT_VALIDATION_FILE_NAME)
      )
      test_df = X.assign(y=y).sample(n=100, random_state=42)
      test_df.to_parquet(
          split_step_output_dir.joinpath(_OUTPUT_TEST_FILE_NAME)
      )
  
      if "classification" in request.keywords:
          run_id, model = train_and_log_classification_model()
      else:
          run_id, model = train_and_log_model()
      train_step_output_dir = tmp_recipe_exec_path.joinpath(
          "steps", "train", "outputs"
      )
      train_step_output_dir.mkdir(parents=True)
      train_step_output_dir.joinpath("run_id").write_text(run_id)
      output_model_path = train_step_output_dir.joinpath(
          TrainStep.SKLEARN_MODEL_ARTIFACT_RELATIVE_PATH
      )
      if os.path.exists(output_model_path) and os.path.isdir(
          output_model_path
      ):
          shutil.rmtree(output_model_path)
      mlflow.sklearn.save_model(model, output_model_path)
  
imports: import os
  import shutil
  from pathlib import Path
  from unittest import mock
  import pytest
  from sklearn.datasets import load_diabetes, load_iris
  import mlflow
  from mlflow.exceptions import MlflowException
  from mlflow.recipes.steps.evaluate import EvaluateStep
  from mlflow.recipes.steps.split import _OUTPUT_TEST_FILE_NAME, _OUTPUT_VALIDATION_FILE_NAME
  from mlflow.recipes.steps.train import TrainStep
  from mlflow.recipes.utils import _RECIPE_CONFIG_FILE_NAME
  from mlflow.utils.file_utils import read_yaml
  from tests.recipes.helper_functions import train_and_log_classification_model, train_and_log_model
label_desc: Use image node_id 1 for calling a global function with example usage: evaluation_inputs(request, tmp_recipe_exec_path) without return types