docstr: Returns a callback function which can be used to attach to DPOptimizer
  Args:
      sample_rate: Expected sampling rate used for accounting
docstr_tok: ["Returns","a","callback","function","which","can","be","used","to","attach","to","DPOptimizer","Args",":","sample_rate",":","Expected","sampling","rate","used","for","accounting"]
code: def get_optimizer_hook_fn(
      self, sample_rate: float
  ) -> Callable[[DPOptimizer], None]:
      """
      Returns a callback function which can be used to attach to DPOptimizer
      Args:
          sample_rate: Expected sampling rate used for accounting
      """
  
      def hook_fn(optim: DPOptimizer):
          # This works for Poisson for both single-node and distributed
          # The reason is that the sample rate is the same in both cases (but in
          # distributed mode, each node samples among a subset of the data)
          self.step(
              noise_multiplier=optim.noise_multiplier,
              sample_rate=sample_rate * optim.accumulated_iterations,
          )
  
      return hook_fn
  
file_path: opacus/opacus/accountants/accountant.py
imports: import abc
  from collections import OrderedDict
  from copy import deepcopy
  from typing import Any, Callable, Mapping, TypeVar
  from opacus.optimizers import DPOptimizer
label_desc: Use image node_id 6 for calling the IAccountant obj's underlying member method code with example usage: obj.get_optimizer_hook_fn(sample_rate) and returns: hook_fn