class_docstr: TOODHead used in `TOOD: Task-aligned One-stage Object Detection.
  
  <https://arxiv.org/abs/2108.07755>`_.
  
  TOOD uses Task-aligned head (T-head) and is optimized by Task Alignment
  Learning (TAL).
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      num_dcn (int): Number of deformable convolution in the head.
          Defaults to 0.
      anchor_type (str): If set to ``anchor_free``, the head will use centers
          to regress bboxes. If set to ``anchor_based``, the head will
          regress bboxes based on anchors. Defaults to ``anchor_free``.
      initial_loss_cls (:obj:`ConfigDict` or dict): Config of initial loss.
  
  Example:
      >>> self = TOODHead(11, 7)
      >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
      >>> cls_score, bbox_pred = self.forward(feats)
      >>> assert len(cls_score) == len(self.scales)
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docstr: Sampling the feature x according to offset.
  
  Args:
      feat (Tensor): Feature
      offset (Tensor): Spatial offset for feature sampling
docstr_tok: ["Sampling","the","feature","x","according","to","offset",".","Args",":","feat","(","Tensor",")",":","Feature","offset","(","Tensor",")",":","Spatial","offset","for","feature","sampling"]
code: def deform_sampling(self, feat: Tensor, offset: Tensor) -> Tensor:
      """Sampling the feature x according to offset.
  
      Args:
          feat (Tensor): Feature
          offset (Tensor): Spatial offset for feature sampling
      """
      # it is an equivalent implementation of bilinear interpolation
      b, c, h, w = feat.shape
      weight = feat.new_ones(c, 1, 1, 1)
      y = deform_conv2d(feat, offset, weight, 1, 0, 1, c, c)
      return y
  
imports: from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, Scale
  from mmcv.ops import deform_conv2d
  from mmengine import MessageHub
  from mmengine.config import ConfigDict
  from mmengine.model import bias_init_with_prob, normal_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures.bbox import distance2bbox
  from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptInstanceList, reduce_mean
  from ..task_modules.prior_generators import anchor_inside_flags
  from ..utils import filter_scores_and_topk, images_to_levels, multi_apply, sigmoid_geometric_mean, unmap
  from .atss_head import ATSSHead
label_desc: Use image node_id 5 for calling the TOODHead obj's underlying member method code with example usage: obj.deform_sampling(feat, offset) and returns: y