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 _zero_grads(self, inputs):
      if isinstance(inputs, torch.Tensor):
          if inputs.grad is not None:
              inputs.grad.data.zero_()
      else:
          for t in inputs:
              if t.grad is not None:
                  t.grad.data.zero_()
  
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 34 for calling the PipelineEngine obj's underlying member method code with example usage: obj._zero_grads(inputs) without return types