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 __init__(
      self,
      num_features: int,
      eps: float = 1e-5,
  ):
      super().__init__()
      _log_api_usage_once(self)
      self.eps = eps
      self.register_buffer("weight", torch.ones(num_features))
      self.register_buffer("bias", torch.zeros(num_features))
      self.register_buffer("running_mean", torch.zeros(num_features))
      self.register_buffer("running_var", torch.ones(num_features))
  
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 1 to create a new FrozenBatchNorm2d object from inherited base classes: torch.nn with example: obj = FrozenBatchNorm2d(num_features, eps)