class_docstr: Implementation of ``ClientAlgo`` to allow federated learning with MONAI bundle configurations.
  
  Args:
      bundle_root: directory path of the bundle.
      local_epochs: number of local epochs to execute during each round of local training; defaults to 1.
      send_weight_diff: whether to send weight differences rather than full weights; defaults to `True`.
      config_train_filename: bundle training config path relative to bundle_root. can be a list of files.
          defaults to "configs/train.json". only useful when `train_workflow` is None.
      train_kwargs: other args of the `ConfigWorkflow` of train, except for `config_file`, `meta_file`,
          `logging_file`, `workflow_type`. only useful when `train_workflow` is None.
      config_evaluate_filename: bundle evaluation config path relative to bundle_root. can be a list of files.
          if "default", ["configs/train.json", "configs/evaluate.json"] will be used.
          this arg is only useful when `eval_workflow` is None.
      eval_kwargs: other args of the `ConfigWorkflow` of evaluation, except for `config_file`, `meta_file`,
          `logging_file`, `workflow_type`. only useful when `eval_workflow` is None.
      config_filters_filename: filter configuration file. Can be a list of files; defaults to `None`.
      disable_ckpt_loading: do not use any CheckpointLoader if defined in train/evaluate configs; defaults to `True`.
      best_model_filepath: location of best model checkpoint; defaults "models/model.pt" relative to `bundle_root`.
      final_model_filepath: location of final model checkpoint; defaults "models/model_final.pt" relative to `bundle_root`.
      save_dict_key: If a model checkpoint contains several state dicts,
          the one defined by `save_dict_key` will be returned by `get_weights`; defaults to "model".
          If all state dicts should be returned, set `save_dict_key` to None.
      data_stats_transform_list: transforms to apply for the data stats result.
      eval_workflow_name: the workflow name corresponding to the "config_evaluate_filename", default to "train"
          as the default "config_evaluate_filename" overrides the train workflow config.
          this arg is only useful when `eval_workflow` is None.
      train_workflow: the bundle workflow to execute training, if None, will create a `ConfigWorkflow` internally
          based on `config_train_filename` and `train_kwargs`.
      eval_workflow: the bundle workflow to execute evaluation, if None, will create a `ConfigWorkflow` internally
          based on `config_evaluate_filename`, `eval_kwargs`, `eval_workflow_name`.
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docstr: Abort the training or evaluation.
  Args:
      extra: Dict with additional information that can be provided by the FL system.
docstr_tok: ["Abort","the","training","or","evaluation",".","Args",":","extra",":","Dict","with","additional","information","that","can","be","provided","by","the","FL","system","."]
code: def abort(self, extra=None):
      """
      Abort the training or evaluation.
      Args:
          extra: Dict with additional information that can be provided by the FL system.
      """
      self.logger.info(
          f"Aborting {self.client_name} during {self.phase} phase."
      )
      if isinstance(self.trainer, Trainer):
          self.logger.info(f"Aborting {self.client_name} trainer...")
          self.trainer.interrupt()
      if isinstance(self.evaluator, Trainer):
          self.logger.info(f"Aborting {self.client_name} evaluator...")
          self.evaluator.interrupt()
  
imports: from __future__ import annotations
  import os
  import time
  from collections.abc import Mapping, MutableMapping
  from typing import Any, cast
  import torch
  import torch.distributed
  from monai.apps.auto3dseg.data_analyzer import DataAnalyzer
  from monai.apps.utils import get_logger
  from monai.auto3dseg import SegSummarizer
  from monai.bundle import BundleWorkflow, ConfigComponent, ConfigItem, ConfigParser, ConfigWorkflow
  from monai.engines import SupervisedEvaluator, SupervisedTrainer, Trainer
  from monai.fl.client import ClientAlgo, ClientAlgoStats
  from monai.fl.utils.constants import ExtraItems, FiltersType, FlPhase, FlStatistics, ModelType, WeightType
  from monai.fl.utils.exchange_object import ExchangeObject
  from monai.networks.utils import copy_model_state, get_state_dict
  from monai.utils import min_version, require_pkg
  from monai.utils.enums import DataStatsKeys
label_desc: Use image node_id 6 for calling the MonaiAlgo obj's underlying member method code with example usage: obj.abort(extra) without return types