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 _load_from_state_dict(
      self,
      state_dict: dict,
      prefix: str,
      local_metadata: dict,
      strict: bool,
      missing_keys: List[str],
      unexpected_keys: List[str],
      error_msgs: List[str],
  ):
      num_batches_tracked_key = prefix + "num_batches_tracked"
      if num_batches_tracked_key in state_dict:
          del state_dict[num_batches_tracked_key]
  
      super()._load_from_state_dict(
          state_dict,
          prefix,
          local_metadata,
          strict,
          missing_keys,
          unexpected_keys,
          error_msgs,
      )
  
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 2 for calling the FrozenBatchNorm2d obj's underlying member method code with example usage: obj._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) without return types