class_docstr: Asymmetric Fusion Non-local Block(AFNB)
  
  Args:
      low_in_channels (int): Input channels of lower level feature,
          which is the key feature for self-attention.
      high_in_channels (int): Input channels of higher level feature,
          which is the query feature for self-attention.
      channels (int): Output channels of key/query transform.
      out_channels (int): Output channels.
          and query projection.
      query_scales (tuple[int]): The scales of query feature map.
          Default: (1,)
      key_pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
          Module of key feature.
      conv_cfg (dict|None): Config of conv layers.
      norm_cfg (dict|None): Config of norm layers.
      act_cfg (dict|None): Config of activation layers.
docstr: Forward function.
docstr_tok: ["Forward","function","."]
code: def forward(self, low_feats, high_feats):
      """Forward function."""
      priors = [stage(high_feats, low_feats) for stage in self.stages]
      context = torch.stack(priors, dim=0).sum(dim=0)
      output = self.bottleneck(torch.cat([context, high_feats], 1))
      return output
  
imports: import torch
  import torch.nn
  from annotator.uniformer.mmcv.cnn import ConvModule
  from ..builder import HEADS
  from ..utils import SelfAttentionBlock
  from .decode_head import BaseDecodeHead
label_desc: Use image node_id 2 for calling the AFNB obj's underlying member method code with example usage: obj.forward(low_feats, high_feats) and returns: output