class_docstr: Provide an abstract base class for defining algo to run on any platform.
  To define a new algo script, subclass this class and implement the
  following abstract methods:
  
      - self.train()
      - self.get_weights()
      - self.evaluate()
      - self.get_data_stats() (optional, inherited from `ClientAlgoStats`)
  
  initialize(), abort(), and finalize() - inherited from `ClientAlgoStats` - can be optionally be implemented
  to help with lifecycle management of the class object.
docstr: Train network and produce new network from train data.
  
  Args:
      data: ExchangeObject containing current network weights to base training on.
      extra: Dict with additional information that can be provided by the FL system.
  
  Returns:
      None
docstr_tok: ["Train","network","and","produce","new","network","from","train","data",".","Args",":","data",":","ExchangeObject","containing","current","network","weights","to","base","training","on",".","extra",":","Dict","with","additional","information","that","can","be","provided","by","the","FL","system",".","Returns",":","None"]
code: def train(
      self, data: ExchangeObject, extra: dict | None = None
  ) -> None:
      """
      Train network and produce new network from train data.
  
      Args:
          data: ExchangeObject containing current network weights to base training on.
          extra: Dict with additional information that can be provided by the FL system.
  
      Returns:
          None
      """
      raise NotImplementedError(
          f"Subclass {self.__class__.__name__} must implement this method."
      )
  
file_path: MONAI/monai/fl/client/client_algo.py
imports: from __future__ import annotations
  from monai.fl.utils.exchange_object import ExchangeObject
label_desc: Use image node_id 1 for calling the ClientAlgo obj's underlying member method code with example usage: obj.train(data, extra) without return types