class_docstr: A ModulatedDeformable Conv Encapsulation that acts as normal Conv
  layers.
  
  Args:
      in_channels (int): Same as nn.Conv2d.
      out_channels (int): Same as nn.Conv2d.
      kernel_size (int or tuple[int]): Same as nn.Conv2d.
      stride (int): Same as nn.Conv2d, while tuple is not supported.
      padding (int): Same as nn.Conv2d, while tuple is not supported.
      dilation (int): Same as nn.Conv2d, while tuple is not supported.
      groups (int): Same as nn.Conv2d.
      bias (bool or str): If specified as `auto`, it will be decided by the
          norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
          False.
class_docstr_tok: ["A","ModulatedDeformable","Conv","Encapsulation","that","acts","as","normal","Conv","layers",".","Args",":","in_channels","(","int",")",":","Same","as","nn.Conv2d",".","out_channels","(","int",")",":","Same","as","nn.Conv2d",".","kernel_size","(","int","or","tuple","[","int","]",")",":","Same","as","nn.Conv2d",".","stride","(","int",")",":","Same","as","nn.Conv2d",",","while","tuple","is","not","supported",".","padding","(","int",")",":","Same","as","nn.Conv2d",",","while","tuple","is","not","supported",".","dilation","(","int",")",":","Same","as","nn.Conv2d",",","while","tuple","is","not","supported",".","groups","(","int",")",":","Same","as","nn.Conv2d",".","bias","(","bool","or","str",")",":","If","specified","as","`","auto","`",",","it","will","be","decided","by","the","norm_cfg",".","Bias","will","be","set","as","True","if","norm_cfg","is","None",",","otherwise","False","."]
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:
          # the key is different in early versions
          # In version < 2, ModulatedDeformConvPack
          # loads previous benchmark models.
          if (
              prefix + "conv_offset.weight" not in state_dict
              and prefix[:-1] + "_offset.weight" in state_dict
          ):
              state_dict[
                  prefix + "conv_offset.weight"
              ] = state_dict.pop(prefix[:-1] + "_offset.weight")
          if (
              prefix + "conv_offset.bias" not in state_dict
              and prefix[:-1] + "_offset.bias" in state_dict
          ):
              state_dict[prefix + "conv_offset.bias"] = state_dict.pop(
                  prefix[:-1] + "_offset.bias"
              )
  
      if version is not None and version > 1:
          print_log(
              f'ModulatedDeformConvPack {prefix.rstrip(".")} is upgraded to '
              "version 2.",
              logger="root",
          )
  
      super()._load_from_state_dict(
          state_dict,
          prefix,
          local_metadata,
          strict,
          missing_keys,
          unexpected_keys,
          error_msgs,
      )
  
imports: import math
  import torch
  import torch.nn
  from torch.autograd import Function
  from torch.autograd.function import once_differentiable
  from torch.nn.modules.utils import _pair, _single
  from annotator.uniformer.mmcv.utils import deprecated_api_warning
  from ..cnn import CONV_LAYERS
  from ..utils import ext_loader, print_log
label_desc: Use image node_id 4 for calling the ModulatedDeformConv2dPack 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