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_buffers(
      self, shapes_and_dtypes, requires_grad=False, num_buffers=-1
  ):
      buffers = []
      if num_buffers == -1:
          num_buffers = self.num_pipe_buffers
      for count in range(num_buffers):
          buffer = []
          for shape, dtype in shapes_and_dtypes:
              buffer.append(
                  self._allocate_zeros(
                      shape, dtype=dtype, requires_grad=requires_grad
                  )
              )
          buffers.append(buffer)
      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 37 for calling the PipelineEngine obj's underlying member method code with example usage: obj._allocate_buffers(shapes_and_dtypes, requires_grad, num_buffers) and returns: buffers