code: def forward(self, inputs):
      x = paddle.concat([inputs["img"], inputs["trimap"] / 255], axis=1)
      embedding, mid_fea = self.encoder(x)
      alpha_pred = self.decoder(embedding, mid_fea)
  
      if self.training:
          logit_dict = {
              "alpha_pred": alpha_pred,
          }
          loss_dict = {}
          alpha_gt = inputs["alpha"]
          loss_dict["alpha"] = F.l1_loss(alpha_pred, alpha_gt)
          loss_dict["all"] = loss_dict["alpha"]
          return logit_dict, loss_dict
  
      return alpha_pred
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg import utils
  from paddleseg.cvlibs import manager, param_init
  from ppmatting.models.layers import GuidedCxtAtten
label_desc: Use image node_id 2 for calling the GCABaseline obj's underlying member method code with example usage: obj.forward(inputs) and returns: alpha_pred, logit_dict,  loss_dict