docstr: Attempts to pre-load the checkpoint file to memory, with the source path determined in this priority:
  
  1. from HPC weights if `checkpoint_path` is ``None`` and on SLURM or passed keyword `"hpc"`.
  2. from fault-tolerant auto-saved checkpoint if found
  3. from `checkpoint_path` file if provided
  4. don't restore
docstr_tok: ["Attempts","to","pre-load","the","checkpoint","file","to","memory",",","with","the","source","path","determined","in","this","priority",":","1.","from","HPC","weights","if","`","checkpoint_path","`","is","``","None","``","and","on","SLURM","or","passed","keyword","`","``","hpc","''","`",".","2.","from","fault-tolerant","auto-saved","checkpoint","if","found","3.","from","`","checkpoint_path","`","file","if","provided","4.","do","n't","restore"]
code: def resume_start(
      self, checkpoint_path: Optional[_PATH] = None
  ) -> None:
      """Attempts to pre-load the checkpoint file to memory, with the source path determined in this priority:
  
      1. from HPC weights if `checkpoint_path` is ``None`` and on SLURM or passed keyword `"hpc"`.
      2. from fault-tolerant auto-saved checkpoint if found
      3. from `checkpoint_path` file if provided
      4. don't restore
  
      """
      self._ckpt_path = checkpoint_path
      if not checkpoint_path:
          log.debug(
              "`checkpoint_path` not specified. Skipping checkpoint loading."
          )
          return
  
      rank_zero_info(
          f"Restoring states from the checkpoint path at {checkpoint_path}"
      )
      with pl_legacy_patch():
          loaded_checkpoint = self.trainer.strategy.load_checkpoint(
              checkpoint_path
          )
      self._loaded_checkpoint = _pl_migrate_checkpoint(
          loaded_checkpoint, checkpoint_path
      )
  
imports: import logging
  import os
  import re
  from typing import Any, Dict, Optional
  import torch
  from fsspec.core import url_to_fs
  from fsspec.implementations.local import LocalFileSystem
  from torch import Tensor
  import lightning.pytorch
  from lightning.fabric.plugins.environments.slurm import SLURMEnvironment
  from lightning.fabric.utilities.cloud_io import _is_dir, get_filesystem
  from lightning.fabric.utilities.types import _PATH
  from lightning.pytorch.callbacks import ModelCheckpoint
  from lightning.pytorch.plugins.precision import MixedPrecision
  from lightning.pytorch.trainer import call
  from lightning.pytorch.trainer.states import TrainerFn
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.imports import _OMEGACONF_AVAILABLE
  from lightning.pytorch.utilities.migration import pl_legacy_patch
  from lightning.pytorch.utilities.migration.utils import _pl_migrate_checkpoint
  from lightning.pytorch.utilities.rank_zero import rank_zero_info, rank_zero_warn
label_desc: Use image node_id 3 for calling the _CheckpointConnector obj's underlying member method code with example usage: obj.resume_start(checkpoint_path) without return types