docstr: The manages the versions of the experiments based on the name. It is a simple dictionary(yaml) based lookup.
  Primary purpose is to avoid overwriting of saved models while running the training without changing the
  experiment name.
  
  Args:
      exp_version_manager (str, optional): The path of the yml file which acts as version control.
          Defaults to ".pt_tmp/exp_version_manager.yml".
docstr_tok: ["The","manages","the","versions","of","the","experiments","based","on","the","name",".","It","is","a","simple","dictionary","(","yaml",")","based","lookup",".","Primary","purpose","is","to","avoid","overwriting","of","saved","models","while","running","the","training","without","changing","the","experiment","name",".","Args",":","exp_version_manager","(","str",",","optional",")",":","The","path","of","the","yml","file","which","acts","as","version","control",".","Defaults","to","``",".pt_tmp\/exp_version_manager.yml","''","."]
code: def __init__(
      self,
      exp_version_manager: str = ".pt_tmp/exp_version_manager.yml",
  ) -> None:
      """The manages the versions of the experiments based on the name. It is a simple dictionary(yaml) based lookup.
      Primary purpose is to avoid overwriting of saved models while running the training without changing the
      experiment name.
  
      Args:
          exp_version_manager (str, optional): The path of the yml file which acts as version control.
              Defaults to ".pt_tmp/exp_version_manager.yml".
      """
      super().__init__()
      self._exp_version_manager = exp_version_manager
      if os.path.exists(exp_version_manager):
          self.exp_version_manager = OmegaConf.load(exp_version_manager)
      else:
          self.exp_version_manager = OmegaConf.create({})
          os.makedirs(
              os.path.split(exp_version_manager)[0], exist_ok=True
          )
          with open(self._exp_version_manager, "w") as file:
              OmegaConf.save(config=self.exp_version_manager, f=file)
  
imports: import os
  import re
  from dataclasses import MISSING, dataclass, field
  from typing import Any, Dict, Iterable, List, Optional
  from omegaconf import OmegaConf
  from pytorch_tabular.models.common import heads
  from pytorch_tabular.utils import get_logger
label_desc: Use image node_id 1 to create a new ExperimentRunManager object  with example: obj = ExperimentRunManager(exp_version_manager)