code: def _get_or_create_virtualenv(
      local_model_path,
      env_id=None,
      env_root_dir=None,
      capture_output=False,
      pip_requirements_override=None,
  ):
      """Restores an MLflow model's environment with pyenv and virtualenv and returns a command
      to activate it.
  
      Args:
          local_model_path: Local directory containing the model artifacts.
          env_id: Optional string that is added to the contents of the yaml file before
              calculating the hash. It can be used to distinguish environments that have the
              same conda dependencies but are supposed to be different based on the context.
              For example, when serving the model we may install additional dependencies to the
              environment after the environment has been activated.
          pip_requirements_override: If specified, install the specified python dependencies to
              the environment (upgrade if already installed).
  
      Returns:
          Command to activate the created virtualenv environment
          (e.g. "source /path/to/bin/activate").
  
      """
      _validate_pyenv_is_available()
      _validate_virtualenv_is_available()
  
      # Read environment information
      local_model_path = Path(local_model_path)
      python_env = _get_python_env(local_model_path)
  
      extra_env = _get_virtualenv_extra_env_vars(env_root_dir)
      if env_root_dir is not None:
          virtual_envs_root_path = (
              Path(env_root_dir) / _VIRTUALENV_ENVS_DIR
          )
          pyenv_root_path = Path(env_root_dir) / _PYENV_ROOT_DIR
          pyenv_root_path.mkdir(parents=True, exist_ok=True)
          pyenv_root_dir = str(pyenv_root_path)
      else:
          virtual_envs_root_path = Path(_get_mlflow_virtualenv_root())
          pyenv_root_dir = None
  
      virtual_envs_root_path.mkdir(parents=True, exist_ok=True)
  
      # Create an environment
      python_bin_path = _install_python(
          python_env.python,
          pyenv_root=pyenv_root_dir,
          capture_output=capture_output,
      )
      env_name = _get_virtualenv_name(
          python_env, local_model_path, env_id
      )
      env_dir = virtual_envs_root_path / env_name
      try:
          activate_cmd = _create_virtualenv(
              local_model_path,
              python_bin_path,
              env_dir,
              python_env,
              extra_env=extra_env,
              capture_output=capture_output,
          )
  
          # Install additional dependencies specified by `requirements_override`
          if pip_requirements_override:
              _logger.info(
                  "Installing additional dependencies specified by "
                  f"pip_requirements_override: {pip_requirements_override}"
              )
              cmd = _join_commands(
                  activate_cmd,
                  f"python -m pip install --quiet -U {' '.join(pip_requirements_override)}",
              )
              _exec_cmd(
                  cmd,
                  capture_output=capture_output,
                  extra_env=extra_env,
              )
  
          return activate_cmd
  
      except:
          _logger.warning(
              "Encountered unexpected error while creating %s", env_dir
          )
          if env_dir.exists():
              _logger.warning("Attempting to remove %s", env_dir)
              shutil.rmtree(env_dir, ignore_errors=True)
              msg = (
                  "Failed to remove %s"
                  if env_dir.exists()
                  else "Successfully removed %s"
              )
              _logger.warning(msg, env_dir)
          raise
  
imports: import logging
  import os
  import re
  import shutil
  import sys
  import tempfile
  import uuid
  from pathlib import Path
  from packaging.version import Version
  import mlflow
  from mlflow.environment_variables import MLFLOW_ENV_ROOT
  from mlflow.exceptions import MlflowException
  from mlflow.models.model import MLMODEL_FILE_NAME, Model
  from mlflow.utils.conda import _PIP_CACHE_DIR
  from mlflow.utils.databricks_utils import is_in_databricks_runtime
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _get_mlflow_env_name, _get_pip_install_mlflow, _PythonEnv
  from mlflow.utils.file_utils import remove_on_error
  from mlflow.utils.process import _IS_UNIX, _exec_cmd, _join_commands
  from mlflow.utils.requirements_utils import _parse_requirements
label_desc: Use image node_id 16 for calling a global function with example usage: _get_or_create_virtualenv(local_model_path, env_id, env_root_dir, capture_output, pip_requirements_override) and returns: activate_cmd