class_docstr: Common base of _InstanceNorm and _BatchNorm.
class_docstr_tok: ["Common","base","of","_InstanceNorm","and","_BatchNorm","."]
code: def _load_from_state_dict(
      self,
      state_dict,
      prefix,
      local_metadata,
      strict,
      missing_keys,
      unexpected_keys,
      error_msgs,
  ):
      version = local_metadata.get("version", None)
  
      if (version is None or version < 2) and self.track_running_stats:
          # at version 2: added num_batches_tracked buffer
          #               this should have a default value of 0
          num_batches_tracked_key = prefix + "num_batches_tracked"
          if num_batches_tracked_key not in state_dict:
              state_dict[num_batches_tracked_key] = (
                  self.num_batches_tracked
                  if self.num_batches_tracked is not None
                  and self.num_batches_tracked.device
                  != torch.device("meta")
                  else torch.tensor(0, dtype=torch.long)
              )
  
      super()._load_from_state_dict(
          state_dict,
          prefix,
          local_metadata,
          strict,
          missing_keys,
          unexpected_keys,
          error_msgs,
      )
  
imports: from typing import Optional, Any
  import torch
  from torch import Tensor
  from torch.nn.parameter import Parameter, UninitializedParameter, UninitializedBuffer
  from ..None import functional
  from ..None import init
  from ._functions import SyncBatchNorm
  from .lazy import LazyModuleMixin
  from .module import Module
label_desc: Use image node_id 6 for calling the _NormBase 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