class_docstr: A training engine hybrid pipeline, data, and model parallel training.
  
  This engine is created by ``deepspeed.initialize()`` when a :class:`PipelineModule`
  is provided.
docstr: Execute a post-processing function on input data.
  
  Args:
      fn (function): The function to run.
docstr_tok: ["Execute","a","post-processing","function","on","input","data",".","Args",":","fn","(","function",")",":","The","function","to","run","."]
code: def set_batch_fn(self, fn):
      """Execute a post-processing function on input data.
  
      Args:
          fn (function): The function to run.
      """
      self.batch_fn = fn
  
imports: from types import MethodType
  import torch
  from deepspeed import comm
  from deepspeed.utils import logger
  from deepspeed.utils.timer import ThroughputTimer
  from ..engine import DeepSpeedEngine, MEMORY_OPT_ALLREDUCE_SIZE
  from ..utils import PartitionedTensor
  from ..dataloader import RepeatingLoader
  from .module import PipelineModule, PipelineError
  from .None import p2p
  from .None import schedule
label_desc: Use image node_id 19 for calling the PipelineEngine obj's underlying member method code with example usage: obj.set_batch_fn(fn) without return types