class_docstr: BatchNorm2d where the batch statistics and the affine parameters are fixed
code: def __init__(self, n):
      super(FrozenBatchNorm2d, self).__init__()
      self.register_buffer("weight", torch.ones(n))
      self.register_buffer("bias", torch.zeros(n))
      self.register_buffer("running_mean", torch.zeros(n))
      self.register_buffer("running_var", torch.ones(n))
  
imports: import functools
  import torch
  import torch.distributed
  from torch import nn
  from apex import check_cudnn_version_and_warn
  import fast_bottleneck
  import nccl_p2p_cuda
label_desc: Use image node_id 1 to create a new FrozenBatchNorm2d object from inherited base classes: torch.jit with example: obj = FrozenBatchNorm2d(n)