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 forward(self, x):
      # move reshapes to the beginning
      # to make it fuser-friendly
      w = self.weight.reshape(1, -1, 1, 1)
      b = self.bias.reshape(1, -1, 1, 1)
      rv = self.running_var.reshape(1, -1, 1, 1)
      rm = self.running_mean.reshape(1, -1, 1, 1)
      eps = 1e-5
      scale = w * (rv + eps).rsqrt()
      bias = b - rm * scale
      return x * scale + bias
  
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 3 for calling the FrozenBatchNorm2d obj's underlying member method code with example usage: obj.forward(x) and returns: unknown