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: Adjust the global batch size by increasing or decreasing the number of
  micro-batches (i.e., gradient accumulation steps). The size of each micro-batch
  (i.e., ``train_micro_batch_size_per_gpu``) is not changed.
  Args:
      train_batch_size (int): The new global batch size for training.
  Raises:
      ValueError: if ``train_batch_size`` is not divisible by the
          configured micro-batch size and data parallelism.
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code: def set_train_batch_size(self, train_batch_size):
      """Adjust the global batch size by increasing or decreasing the number of
      micro-batches (i.e., gradient accumulation steps). The size of each micro-batch
      (i.e., ``train_micro_batch_size_per_gpu``) is not changed.
      Args:
          train_batch_size (int): The new global batch size for training.
      Raises:
          ValueError: if ``train_batch_size`` is not divisible by the
              configured micro-batch size and data parallelism.
      """
      super().set_train_batch_size(train_batch_size)
      self.micro_batches = self.gradient_accumulation_steps()
  
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 11 for calling the PipelineEngine obj's underlying member method code with example usage: obj.set_train_batch_size(train_batch_size) without return types