docstr: Checkpoint manager. Saves to working directory with check_name
  Arguments
      checkpoint_file {str} -- Path to the checkpoint
      save_every_iter {bool} -- [description] (default: {True})
docstr_tok: ["Checkpoint","manager",".","Saves","to","working","directory","with","check_name","Arguments","checkpoint_file","{","str","}","--","Path","to","the","checkpoint","save_every_iter","{","bool","}","--","[","description","]","(","default",":","{","True","}",")"]
code: def __init__(
      self, checkpoint_file: str, save_every_iter: bool = True
  ):
      """Checkpoint manager. Saves to working directory with check_name
      Arguments
          checkpoint_file {str} -- Path to the checkpoint
          save_every_iter {bool} -- [description] (default: {True})
      """
      self._check_path = checkpoint_file
      self._filled = False
      self.run_config: Optional[Dict] = None
      self.models: Dict[str, Any] = {}
      self.stats: Dict[str, List[Any]] = {
          "train": [],
          "test": [],
          "val": [],
      }
      self.optimizer: Optional[Tuple[str, Any]] = None
      self.schedulers: Dict[str, Any] = {}
      self.dataset_properties: Dict = {}
  
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import os
  import torch
  import logging
  import copy
  import glob
  import shutil
  from omegaconf import OmegaConf
  from omegaconf import DictConfig
  from torch_points3d.models import model_interface
  from torch_points3d.utils.colors import COLORS, colored_print
  from torch_points3d.core.schedulers.lr_schedulers import instantiate_scheduler
  from torch_points3d.core.schedulers.bn_schedulers import instantiate_bn_scheduler
  from torch_points3d.models.model_factory import instantiate_model
label_desc: Use image node_id 1 to create a new Checkpoint object  with example: obj = Checkpoint(checkpoint_file, save_every_iter)