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: Ensure that each pipeline buffer has at least ``num_buffers`` slots.
  
  This method only reserves slots and does not allocate tensors.
  
  Args:
      num_buffers (int): The number of buffers to reserve.
docstr_tok: ["Ensure","that","each","pipeline","buffer","has","at","least","``","num_buffers","``","slots",".","This","method","only","reserves","slots","and","does","not","allocate","tensors",".","Args",":","num_buffers","(","int",")",":","The","number","of","buffers","to","reserve","."]
code: def _reserve_pipe_buffers(self, num_buffers):
      """Ensure that each pipeline buffer has at least ``num_buffers`` slots.
  
      This method only reserves slots and does not allocate tensors.
  
      Args:
          num_buffers (int): The number of buffers to reserve.
      """
      if self.num_pipe_buffers >= num_buffers:
          return
  
      num_added = num_buffers - self.num_pipe_buffers
      for key in self.pipe_buffers:
          self.pipe_buffers[key].extend([None] * num_added)
      self.num_pipe_buffers = num_buffers
  
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 7 for calling the PipelineEngine obj's underlying member method code with example usage: obj._reserve_pipe_buffers(num_buffers) without return types