code: def _generate_mlflow_version_pinning() -> str:
      """Returns a pinned requirement for the current MLflow version (e.g., "mlflow==3.2.1").
  
      Returns:
          A pinned requirement for the current MLflow version.
  
      """
      if _MLFLOW_TESTING.get():
          # The local PyPI server should be running. It serves a wheel for the current MLflow version.
          return f"mlflow=={VERSION}"
  
      version = Version(VERSION)
      if not version.is_devrelease:
          # mlflow is installed from PyPI.
          return f"mlflow=={VERSION}"
  
      # We reach here when mlflow is installed from the source outside of the MLflow CI environment
      # (e.g., Databricks notebook).
  
      # mlflow installed from the source for development purposes. A dev version (e.g., 2.8.1.dev0)
      # is always a micro-version ahead of the latest release (unless it's manually modified)
      # and can't be installed from PyPI. We therefore subtract 1 from the micro version when running
      # tests.
      return (
          f"mlflow=={version.major}.{version.minor}.{version.micro - 1}"
      )
  
imports: import logging
  import os
  import re
  import subprocess
  import sys
  import tempfile
  import yaml
  from packaging.requirements import InvalidRequirement, Requirement
  from packaging.version import Version
  from mlflow.environment_variables import _MLFLOW_TESTING
  from mlflow.exceptions import MlflowException
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
  from mlflow.utils import PYTHON_VERSION, insecure_hash
  from mlflow.utils.process import _exec_cmd
  from mlflow.utils.requirements_utils import _infer_requirements, _parse_requirements
  from mlflow.version import VERSION
label_desc: Use image node_id 13 for calling a global function with example usage: _generate_mlflow_version_pinning() and returns: str, version, str, version, str, unknown, str, VERSION, str, VERSION