class_docstr: BatchNorm2d where the batch statistics and the affine parameters are fixed.
  
  Copy-paste from torchvision.misc.ops with added eps before rqsrt,
  without which any other models than torchvision.models.resnet[18,34,50,101]
  produce nans.
class_docstr_tok: ["BatchNorm2d","where","the","batch","statistics","and","the","affine","parameters","are","fixed",".","Copy-paste","from","torchvision.misc.ops","with","added","eps","before","rqsrt",",","without","which","any","other","models","than","torchvision.models.resnet","[","18,34,50,101","]","produce","nans","."]
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: from collections import OrderedDict
  import torch
  import torch.nn.functional
  import torchvision
  from torch import nn
  from torchvision.models._utils import IntermediateLayerGetter
  from typing import Dict, List
  from util.misc import NestedTensor, is_main_process
  from .position_encoding import build_position_encoding
label_desc: Use image node_id 1 to create a new FrozenBatchNorm2d object from inherited base classes: torch.nn with example: obj = FrozenBatchNorm2d(n)