class_docstr: Position attention module
code: def forward(self, x):
      batch_size, _, height, width = x.size()
      feat_b = (
          self.conv_b(x)
          .view(batch_size, -1, height * width)
          .permute(0, 2, 1)
      )
      feat_c = self.conv_c(x).view(batch_size, -1, height * width)
      attention_s = self.softmax(torch.bmm(feat_b, feat_c))
      feat_d = self.conv_d(x).view(batch_size, -1, height * width)
      feat_e = torch.bmm(feat_d, attention_s.permute(0, 2, 1)).view(
          batch_size, -1, height, width
      )
      out = self.alpha * 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 _PositionAttentionModule obj's underlying member method code with example usage: obj.forward(x) and returns: out