class_docstr: Adds a simple RPN Head with classification and regression heads
  
  Args:
      in_channels (int): number of channels of the input feature
      num_anchors (int): number of anchors to be predicted
      conv_depth (int, optional): number of convolutions
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:
          for type in ["weight", "bias"]:
              old_key = f"{prefix}conv.{type}"
              new_key = f"{prefix}conv.0.0.{type}"
              if old_key in state_dict:
                  state_dict[new_key] = state_dict.pop(old_key)
  
      super()._load_from_state_dict(
          state_dict,
          prefix,
          local_metadata,
          strict,
          missing_keys,
          unexpected_keys,
          error_msgs,
      )
  
imports: from typing import Dict, List, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from torchvision.ops import boxes, Conv2dNormActivation
  from .None import _utils
  from .anchor_utils import AnchorGenerator
  from .image_list import ImageList
label_desc: Use image node_id 2 for calling the RPNHead 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