code: def construct_train_func(
      train_func: Union[Callable[[], T], Callable[[Dict[str, Any]], T]],
      config: Optional[Dict[str, Any]],
      fn_arg_name: Optional[str] = "train_func",
      discard_returns: bool = False,
  ) -> Callable[[], T]:
      """Validates and constructs the training function to execute.
      Args:
          train_func: The training function to execute.
              This can either take in no arguments or a ``config`` dict.
          config (Optional[Dict]): Configurations to pass into
              ``train_func``. If None then an empty Dict will be created.
          fn_arg_name (Optional[str]): The name of training function to use for error
              messages.
          discard_returns: Whether to discard any returns from train_func or not.
      Returns:
          A valid training function.
      Raises:
          ValueError: if the input ``train_func`` is invalid.
      """
      signature = inspect.signature(train_func)
      num_params = len(signature.parameters)
  
      if discard_returns:
          # Discard any returns from the function so that
          # BackendExecutor doesn't try to deserialize them.
          # Those returns are inaccesible with AIR anyway.
          @functools.wraps(train_func)
          def discard_return_wrapper(*args, **kwargs):
              try:
                  train_func(*args, **kwargs)
              except Exception as e:
                  raise StartTraceback from e
  
          wrapped_train_func = discard_return_wrapper
      else:
          wrapped_train_func = train_func
  
      if num_params > 1:
          err_msg = (
              f"{fn_arg_name} should take in 0 or 1 arguments, but it accepts "
              f"{num_params} arguments instead."
          )
          raise ValueError(err_msg)
      elif num_params == 1:
          config = {} if config is None else config
  
          @functools.wraps(wrapped_train_func)
          def train_fn():
              try:
                  return wrapped_train_func(config)
              except Exception as e:
                  raise StartTraceback from e
  
      else:  # num_params == 0
  
          @functools.wraps(wrapped_train_func)
          def train_fn():
              try:
                  return wrapped_train_func()
              except Exception as e:
                  raise StartTraceback from e
  
      return train_fn
  
imports: import abc
  import functools
  import inspect
  import logging
  import os
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar, Union
  import ray
  from ray.actor import ActorHandle
  from ray.air._internal.util import StartTraceback, find_free_port
  from ray.exceptions import RayActorError
  from ray.types import ObjectRef
label_desc: Use image node_id 5 for calling a global function with example usage: construct_train_func(train_func, config, fn_arg_name, discard_returns) and returns: train_fn, wrapped_train_func, wrapped_train_func