code: def save_or_log_tf_model_by_mlflow128(
      tmp_path, model_type, task_type, save_path=None
  ):
      tf_tests_dir = os.path.dirname(__file__)
      conda_env = get_or_create_conda_env(
          os.path.join(tf_tests_dir, "mlflow-128-tf-26-env.yaml")
      )
      output_data_file_path = os.path.join(tmp_path, "output_data.pkl")
      tracking_uri = mlflow.get_tracking_uri()
      exec_py_path = os.path.join(
          tf_tests_dir, "save_tf_estimator_model.py"
      )
      mlflow_repo_path = os.path.dirname(os.path.dirname(tf_tests_dir))
  
      conda_env.execute(
          f"python {exec_py_path} "
          f"--tracking_uri {tracking_uri} "
          f"--mlflow_repo_path {mlflow_repo_path} "
          f"--model_type {model_type} "
          f"--task_type {task_type} "
          f"--save_path {save_path if save_path else 'none'}",
      )
      return ModelDataInfo(*pd.read_pickle(output_data_file_path))
  
imports: import collections
  import json
  import os
  import iris_data_utils
  import numpy
  import pandas
  import pandas.testing
  import pytest
  import tensorflow
  import mlflow
  import mlflow.pyfunc.scoring_server
  from mlflow import pyfunc
  from mlflow.store.artifact.s3_artifact_repo import S3ArtifactRepository
  from mlflow.tensorflow import _TF2Wrapper
  from mlflow.utils.conda import get_or_create_conda_env
  from mlflow.utils.environment import _mlflow_conda_env
  from tests.helper_functions import assert_array_almost_equal, pyfunc_serve_and_score_model
label_desc: Use image node_id 3 for calling a global function with example usage: save_or_log_tf_model_by_mlflow128(tmp_path, model_type, task_type, save_path) and returns: ModelDataInfo