class_docstr: A BatchNorm layer tracks multiple micro-batches to update running
  statistics per mini-batch.
code: def __init__(
      self,
      num_features: int,
      eps: float = 1e-5,
      momentum: Optional[float] = 0.1,
      affine: bool = True,
      chunks: int = 1,
  ) -> None:
      super().__init__(
          num_features, eps, momentum, affine, track_running_stats=True
      )
  
      self.register_buffer("sum", torch.zeros_like(self.running_mean))
      self.register_buffer(
          "sum_squares", torch.zeros_like(self.running_var)
      )
  
      self.counter = 0
      self.tracked = 0
      self.chunks = chunks
  
file_path: fairscale/fairscale/nn/pipe/batchnorm.py
imports: from typing import Optional, TypeVar, cast
  import torch
  from torch import Tensor, nn
  import torch.nn.functional
  from torch.nn.modules.batchnorm import _BatchNorm
  from .checkpoint import is_recomputing
label_desc: Use image node_id 1 to create a new DeferredBatchNorm object from inherited base classes: _BatchNorm with example: obj = DeferredBatchNorm(num_features, eps, momentum, affine, chunks)