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.
code: def _allocate_buffer(self, shape, num_buffers=-1, **kwargs):
      buffers = []
      if num_buffers == -1:
          num_buffers = self.num_pipe_buffers
      for count in range(num_buffers):
          buffers.append(self._allocate_zeros(shape, **kwargs))
      return 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 36 for calling the PipelineEngine obj's underlying member method code with example usage: obj._allocate_buffer(shape, num_buffers) and returns: buffers