code: def forward(self, x: List[Tensor]) -> Tensor:
      all_results = []
  
      for i, features in enumerate(x):
          results = self._get_result_from_module_list(features, i)
  
          # Permute output from (N, A * K, H, W) to (N, HWA, K).
          N, _, H, W = results.shape
          results = results.view(N, -1, self.num_columns, H, W)
          results = results.permute(0, 3, 4, 1, 2)
          results = results.reshape(
              N, -1, self.num_columns
          )  # Size=(N, HWA, K)
  
          all_results.append(results)
  
      return torch.cat(all_results, dim=1)
  
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 3 for calling the SSDScoringHead obj's underlying member method code with example usage: obj.forward(x) and returns: torch