class_docstr: Detection Head of `ATSS <https://arxiv.org/abs/1912.02424>`_.
  
  ATSS head structure is similar with FCOS, however ATSS use anchor boxes
  and assign label by Adaptive Training Sample Selection instead max-iou.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      pred_kernel_size (int): Kernel size of ``nn.Conv2d``
      stacked_convs (int): Number of stacking convs of the head.
      conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
          convolution layer. Defaults to None.
      norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization
          layer. Defaults to ``dict(type='GN', num_groups=32,
          requires_grad=True)``.
      reg_decoded_bbox (bool): If true, the regression loss would be
          applied directly on decoded bounding boxes, converting both
          the predicted boxes and regression targets to absolute
          coordinates format. Defaults to False. It should be `True` when
          using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
      loss_centerness (:obj:`ConfigDict` or dict): Config of centerness loss.
          Defaults to ``dict(type='CrossEntropyLoss', use_sigmoid=True,
          loss_weight=1.0)``.
      init_cfg (:obj:`ConfigDict` or dict or list[dict] or
          list[:obj:`ConfigDict`]): Initialization config dict.
docstr: Forward features from the upstream network.
  
  Args:
      x (tuple[Tensor]): Features from the upstream network, each is
          a 4D-tensor.
  
  Returns:
      tuple: Usually a tuple of classification scores and bbox prediction
          cls_scores (list[Tensor]): Classification scores for all scale
              levels, each is a 4D-tensor, the channels number is
              num_anchors * num_classes.
          bbox_preds (list[Tensor]): Box energies / deltas for all scale
              levels, each is a 4D-tensor, the channels number is
              num_anchors * 4.
docstr_tok: ["Forward","features","from","the","upstream","network",".","Args",":","x","(","tuple","[","Tensor","]",")",":","Features","from","the","upstream","network",",","each","is","a","4D-tensor",".","Returns",":","tuple",":","Usually","a","tuple","of","classification","scores","and","bbox","prediction","cls_scores","(","list","[","Tensor","]",")",":","Classification","scores","for","all","scale","levels",",","each","is","a","4D-tensor",",","the","channels","number","is","num_anchors","*","num_classes",".","bbox_preds","(","list","[","Tensor","]",")",":","Box","energies","\/","deltas","for","all","scale","levels",",","each","is","a","4D-tensor",",","the","channels","number","is","num_anchors","*","4","."]
code: def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor]]:
      """Forward features from the upstream network.
  
      Args:
          x (tuple[Tensor]): Features from the upstream network, each is
              a 4D-tensor.
  
      Returns:
          tuple: Usually a tuple of classification scores and bbox prediction
              cls_scores (list[Tensor]): Classification scores for all scale
                  levels, each is a 4D-tensor, the channels number is
                  num_anchors * num_classes.
              bbox_preds (list[Tensor]): Box energies / deltas for all scale
                  levels, each is a 4D-tensor, the channels number is
                  num_anchors * 4.
      """
      return multi_apply(self.forward_single, x, self.scales)
  
imports: from typing import List, Optional, Sequence, Tuple
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule, Scale
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptConfigType, OptInstanceList, reduce_mean
  from ..task_modules.prior_generators import anchor_inside_flags
  from ..utils import images_to_levels, multi_apply, unmap
  from .anchor_head import AnchorHead
label_desc: Use image node_id 3 for calling the ATSSHead obj's underlying member method code with example usage: obj.forward(x) and returns: multi_apply