class_docstr: Channel attention module.
class_docstr_tok: ["Channel","attention","module","."]
code: def forward(self, x: paddle.Tensor) -> paddle.Tensor:
      x_shape = paddle.shape(x)
      # query: n, c, h * w
      query = paddle.reshape(x, (0, self.channels, -1))
      # key: n, h * w, c
      key = paddle.reshape(x, (0, self.channels, -1))
      key = paddle.transpose(key, (0, 2, 1))
  
      # sim: n, c, c
      sim = paddle.bmm(query, key)
      # The danet author claims that this can avoid gradient divergence
      sim = (
          paddle.max(sim, axis=-1, keepdim=True).tile(
              [1, 1, self.channels]
          )
          - sim
      )
      sim = F.softmax(sim, axis=-1)
  
      # feat: from (n, c, h * w) to (n, c, h, w)
      value = paddle.reshape(x, (0, self.channels, -1))
      feat = paddle.bmm(sim, value)
      feat = paddle.reshape(
          feat, (0, self.channels, x_shape[2], x_shape[3])
      )
  
      out = self.gamma * feat + x
      return out
  
imports: import os
  from typing import Union, List, Tuple
  import paddle
  from paddle import nn
  import paddle.nn.functional
  import numpy
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  from danet_resnet50_voc.resnet import ResNet50_vd
  import danet_resnet50_voc.layers
label_desc: Use image node_id 2 for calling the CAM obj's underlying member method code with example usage: obj.forward(x) and returns: out