code: def save_for_pretrained(
      hparams,
      min_key=None,
      max_key=None,
      ckpt_predicate=None,
      pretrainer_key="pretrainer",
      checkpointer_key="checkpointer",
  ):
      """
      Saves the necessary files for the pretrained model
      from the best checkpoint found. The goal of this function
      is to export the model for a Pretrainer
  
      Arguments
      ---------
      hparams: dict
          the hyperparameter file
      max_key: str
          Key to use for finding best checkpoint (higher is better).
          By default, passed to ``self.checkpointer.recover_if_possible()``.
      min_key: str
          Key to use for finding best checkpoint (lower is better).
          By default, passed to ``self.checkpointer.recover_if_possible()``.
      ckpt_predicate: callable
          a filter predicate to locate checkpoints
      checkpointer_key: str
          the key under which the checkpointer is stored
      pretrained_key: str
          the key under which the pretrainer is stored
      """
      if any(
          key not in hparams
          for key in [pretrainer_key, checkpointer_key]
      ):
          raise ValueError(
              f"Incompatible hparams: a checkpointer with key {checkpointer_key}"
              f"and a pretrainer with key {pretrainer_key} are required"
          )
      pretrainer = hparams[pretrainer_key]
      checkpointer = hparams[checkpointer_key]
      checkpoint = checkpointer.find_checkpoint(
          min_key=min_key,
          max_key=max_key,
          ckpt_predicate=ckpt_predicate,
      )
      if checkpoint:
          logger.info(
              "Saving checkpoint '%s' a pretrained model",
              checkpoint.path,
          )
          pretrainer_keys = set(pretrainer.loadables.keys())
          checkpointer_keys = set(checkpoint.paramfiles.keys())
          keys_to_save = pretrainer_keys & checkpointer_keys
          for key in keys_to_save:
              source_path = checkpoint.paramfiles[key]
              if not os.path.exists(source_path):
                  raise ValueError(
                      f"File {source_path} does not exist in the checkpoint"
                  )
              target_path = pretrainer.paths[key]
              dirname = os.path.dirname(target_path)
              if not os.path.exists(dirname):
                  os.makedirs(dirname)
              if os.path.exists(target_path):
                  os.remove(target_path)
              shutil.copyfile(source_path, target_path)
          saved = True
      else:
          logger.info(
              "Unable to find a matching checkpoint for min_key = %s, max_key = %s",
              min_key,
              max_key,
          )
          checkpoints = checkpointer.list_checkpoints()
          checkpoints_str = "\n".join(
              f"{checkpoint.path}: {checkpoint.meta}"
              for checkpoint in checkpoints
          )
          logger.info("Available checkpoints: %s", checkpoints_str)
          saved = False
  
      return saved
  
imports: import os
  import logging
  import shutil
label_desc: Use image node_id 1 for calling a global function with example usage: save_for_pretrained(hparams, min_key, max_key, ckpt_predicate, pretrainer_key, checkpointer_key) and returns: saved