class_docstr: BatchNorm2d where the batch statistics and the affine parameters are fixed
  
  Args:
      num_features (int): Number of features ``C`` from an expected input of size ``(N, C, H, W)``
      eps (float): a value added to the denominator for numerical stability. Default: 1e-5
code: def forward(self, x: Tensor) -> Tensor:
      # 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)
      scale = w * (rv + self.eps).rsqrt()
      bias = b - rm * scale
      return x * scale + bias
  
imports: import warnings
  from typing import Callable, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from ..utils import _log_api_usage_once, _make_ntuple
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