code: def __init__(
      self,
      in_channels: List[int],
      num_anchors: List[int],
      num_classes: int,
  ):
      cls_logits = nn.ModuleList()
      for channels, anchors in zip(in_channels, num_anchors):
          cls_logits.append(
              nn.Conv2d(
                  channels,
                  num_classes * anchors,
                  kernel_size=3,
                  padding=1,
              )
          )
      _xavier_init(cls_logits)
      super().__init__(cls_logits, num_classes)
  
imports: import warnings
  from collections import OrderedDict
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ...ops import boxes
  from ...transforms._presets import ObjectDetection
  from ...utils import _log_api_usage_once
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _COCO_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface
  from ..vgg import VGG, vgg16, VGG16_Weights
  from .None import _utils
  from .anchor_utils import DefaultBoxGenerator
  from .backbone_utils import _validate_trainable_layers
  from .transform import GeneralizedRCNNTransform
label_desc: Use image node_id 1 to create a new SSDClassificationHead object from inherited base classes: SSDScoringHead with example: obj = SSDClassificationHead(in_channels, num_anchors, num_classes)