code: def forward(
      self, img_feat, alpha_feat, unknown=None, softmax_scale=1.0
  ):
      img_feat = self.guidance_conv(img_feat)
      img_feat = F.interpolate(
          img_feat, scale_factor=1 / self.rate, mode="nearest"
      )
  
      # process unknown mask
      unknown, softmax_scale = self.process_unknown_mask(
          unknown, img_feat, softmax_scale
      )
  
      img_ps, alpha_ps, unknown_ps = self.extract_feature_maps_patches(
          img_feat, alpha_feat, unknown
      )
  
      self_mask = self.get_self_correlation_mask(img_feat)
  
      # split tensors by batch dimension; tuple is returned
      img_groups = paddle.split(img_feat, 1, axis=0)
      img_ps_groups = paddle.split(img_ps, 1, axis=0)
      alpha_ps_groups = paddle.split(alpha_ps, 1, axis=0)
      unknown_ps_groups = paddle.split(unknown_ps, 1, axis=0)
      scale_groups = paddle.split(softmax_scale, 1, axis=0)
      groups = (
          img_groups,
          img_ps_groups,
          alpha_ps_groups,
          unknown_ps_groups,
          scale_groups,
      )
  
      y = []
  
      for img_i, img_ps_i, alpha_ps_i, unknown_ps_i, scale_i in zip(
          *groups
      ):
          # conv for compare
          similarity_map = self.compute_similarity_map(img_i, img_ps_i)
  
          gca_score = self.compute_guided_attention_score(
              similarity_map, unknown_ps_i, scale_i, self_mask
          )
  
          yi = self.propagate_alpha_feature(gca_score, alpha_ps_i)
  
          y.append(yi)
  
      y = paddle.concat(y, axis=0)  # back to the mini-batch
      y = paddle.reshape(y, alpha_feat.shape)
  
      y = self.out_conv(y) + alpha_feat
  
      return y
  
file_path: PaddleSeg/Matting/ppmatting/models/layers/gca_module.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import param_init
label_desc: Use image node_id 3 for calling the GuidedCxtAtten obj's underlying member method code with example usage: obj.forward(img_feat, alpha_feat, unknown, softmax_scale) and returns: y