code: def __init__(
      self,
      eps=1e-5,
      momentum=0.1,
      affine=True,
      track_running_stats=True,
      device=None,
      dtype=None,
  ) -> None:
      factory_kwargs = {"device": device, "dtype": dtype}
      super().__init__(
          # affine and track_running_stats are hardcoded to False to
          # avoid creating tensors that will soon be overwritten.
          0,
          eps,
          momentum,
          False,
          False,
          **factory_kwargs,
      )
      self.affine = affine
      self.track_running_stats = track_running_stats
      if self.affine:
          self.weight = UninitializedParameter(**factory_kwargs)
          self.bias = UninitializedParameter(**factory_kwargs)
      if self.track_running_stats:
          self.running_mean = UninitializedBuffer(**factory_kwargs)
          self.running_var = UninitializedBuffer(**factory_kwargs)
          self.num_batches_tracked = torch.tensor(
              0,
              dtype=torch.long,
              **{
                  k: v
                  for k, v in factory_kwargs.items()
                  if k != "dtype"
              },
          )
  
imports: from typing import Optional, Any
  import torch
  from torch import Tensor
  from torch.nn.parameter import Parameter, UninitializedParameter, UninitializedBuffer
  from ..None import functional
  from ..None import init
  from ._functions import SyncBatchNorm
  from .lazy import LazyModuleMixin
  from .module import Module
label_desc: Use image node_id 1 to create a new _LazyNormBase object from inherited base classes: LazyModuleMixin, _NormBase with example: obj = _LazyNormBase(eps, momentum, affine, track_running_stats, device, dtype)