class_docstr: BatchNorm2d where the batch statistics and the affine parameters are
  fixed.
  
  It contains non-trainable buffers called
  "weight" and "bias", "running_mean", "running_var",
  initialized to perform identity transformation.
  Args:
     num_features (int):  :math:`C` from an expected input of size
          :math:`(N, C, H, W)`.
     eps (float): a value added to the denominator for numerical stability.
          Default: 1e-5
class_docstr_tok: ["BatchNorm2d","where","the","batch","statistics","and","the","affine","parameters","are","fixed",".","It","contains","non-trainable","buffers","called","''","weight","''","and","``","bias","''",",","``","running_mean","''",",","``","running_var","''",",","initialized","to","perform","identity","transformation",".","Args",":","num_features","(","int",")",":",":","math",":","`","C","`","from","an","expected","input","of","size",":","math",":","`","(","N",",","C",",","H",",","W",")","`",".","eps","(","float",")",":","a","value","added","to","the","denominator","for","numerical","stability",".","Default",":","1e-5"]
code: def __repr__(self):
      return "FrozenBatchNorm2d(num_features={}, eps={})".format(
          self.num_features, self.eps
      )
  
file_path: mmdetection/mmdet/models/layers/brick_wrappers.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn.bricks.wrappers import NewEmptyTensorOp, obsolete_torch_version
  from mmdet.registry import MODELS
label_desc: Use image node_id 3 for calling the FrozenBatchNorm2d obj's underlying member method code with example usage: obj.__repr__() and returns: str