class_docstr: The base class for an AllenNLP trainer. It can do pretty much
  anything you want. Your subclass should implement `train`
  and also probably `from_params`.
class_docstr_tok: ["The","base","class","for","an","AllenNLP","trainer",".","It","can","do","pretty","much","anything","you","want",".","Your","subclass","should","implement","`","train","`","and","also","probably","`","from_params","`","."]
docstr: Returns the path to file containing the current best weights.
docstr_tok: ["Returns","the","path","to","file","containing","the","current","best","weights","."]
code: def get_best_weights_path(self) -> Optional[str]:
      """Returns the path to file containing the current best weights."""
      return None
  
file_path: allennlp/allennlp/training/trainer.py
imports: import logging
  import os
  from typing import Any, Dict, Optional, Union, NamedTuple
  import torch.optim.lr_scheduler
  from allennlp.common import Registrable
  from allennlp.common.checks import ConfigurationError, check_for_gpu
  from allennlp.common.util import int_to_device
label_desc: Use image node_id 4 for calling the Trainer obj's underlying member method code with example usage: obj.get_best_weights_path() and returns: None