code: def __init__(self, dense_module):
      super().__init__()
  
      assert not dense_module.training
      assert dense_module.track_running_stats
      assert dense_module.affine
  
      if dense_module.momentum is None:
          self.exponential_average_factor = 0.0
      else:
          self.exponential_average_factor = dense_module.momentum
      self.eps = dense_module.eps
  
      self.register_buffer("weight", dense_module.weight.to_mkldnn())
      self.register_buffer("bias", dense_module.bias.to_mkldnn())
      self.register_buffer(
          "running_mean", dense_module.running_mean.to_mkldnn()
      )
      self.register_buffer(
          "running_var", dense_module.running_var.to_mkldnn()
      )
  
imports: import torch
label_desc: Use image node_id 1 to create a new MkldnnBatchNorm object from inherited base classes: torch.jit with example: obj = MkldnnBatchNorm(dense_module)