code: def __init__(self, in_channels, num_keypoints):
      super().__init__()
      input_features = in_channels
      deconv_kernel = 4
      self.kps_score_lowres = nn.ConvTranspose2d(
          input_features,
          num_keypoints,
          deconv_kernel,
          stride=2,
          padding=deconv_kernel // 2 - 1,
      )
      nn.init.kaiming_normal_(
          self.kps_score_lowres.weight,
          mode="fan_out",
          nonlinearity="relu",
      )
      nn.init.constant_(self.kps_score_lowres.bias, 0)
      self.up_scale = 2
      self.out_channels = num_keypoints
  
imports: from typing import Any, Optional
  import torch
  from torch import nn
  from torchvision.ops import MultiScaleRoIAlign
  from ...ops import misc
  from ...transforms._presets import ObjectDetection
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _COCO_PERSON_CATEGORIES, _COCO_PERSON_KEYPOINT_NAMES
  from .._utils import _ovewrite_value_param, handle_legacy_interface
  from ..resnet import resnet50, ResNet50_Weights
  from ._utils import overwrite_eps
  from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers
  from .faster_rcnn import FasterRCNN
label_desc: Use image node_id 1 to create a new KeypointRCNNPredictor object from inherited base classes: nn with example: obj = KeypointRCNNPredictor(in_channels, num_keypoints)