class_docstr: Channel attention module
code: def forward(self, x):
      batch_size, _, height, width = x.size()
      feat_a = x.view(batch_size, -1, height * width)
      feat_a_transpose = x.view(batch_size, -1, height * width).permute(
          0, 2, 1
      )
      attention = torch.bmm(feat_a, feat_a_transpose)
      attention_new = (
          torch.max(attention, dim=-1, keepdim=True)[0].expand_as(
              attention
          )
          - attention
      )
      attention = self.softmax(attention_new)
  
      feat_e = torch.bmm(attention, feat_a).view(
          batch_size, -1, height, width
      )
      out = self.beta * feat_e + x
  
      return out
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.segbase import SegBaseModel
label_desc: Use image node_id 2 for calling the _ChannelAttentionModule obj's underlying member method code with example usage: obj.forward(x) and returns: out