class_docstr: A model for 
class_docstr_tok: ["A","model","for"]
code: def forward(self, data):
      src = data["img"]
      src_h, src_w = paddle.shape(src)[2:]
      if self.if_refine:
          # It is not need when exporting.
          if isinstance(src_h, paddle.Tensor):
              if (src_h % 4 != 0) or (src_w % 4) != 0:
                  raise ValueError(
                      "The input image must have width and height that are divisible by 4"
                  )
  
      # Downsample src for backbone
      src_sm = F.interpolate(
          src,
          scale_factor=self.backbone_scale,
          mode="bilinear",
          align_corners=False,
      )
  
      # Base
      fea_list = self.backbone(src_sm)
      ##########################
      ### Decoder part - GLANCE
      ##########################
      # psp: N, 512, H/32, W/32
      psp = self.psp_module(fea_list[-1])
      # d6_g: N, 512, H/16, W/16
      d5_g = self.decoder5_g(paddle.concat((psp, fea_list[-1]), 1))
      # d5_g: N, 512, H/8, W/8
      d4_g = self.decoder4_g(paddle.concat((self.psp4(psp), d5_g), 1))
      # d4_g: N, 256, H/4, W/4
      d3_g = self.decoder3_g(paddle.concat((self.psp3(psp), d4_g), 1))
      # d4_g: N, 128, H/2, W/2
      d2_g = self.decoder2_g(paddle.concat((self.psp2(psp), d3_g), 1))
      # d2_g: N, 64, H, W
      d1_g = self.decoder1_g(paddle.concat((self.psp1(psp), d2_g), 1))
      # d0_g: N, 3, H, W
      d0_g = self.decoder0_g(d1_g)
      # The 1st channel is foreground. The 2nd is transition region. The 3rd is background.
      # glance_sigmoid = F.sigmoid(d0_g)
      glance_sigmoid = F.softmax(d0_g, axis=1)
  
      ##########################
      ### Decoder part - FOCUS
      ##########################
      bb = self.bridge_block(fea_list[-1])
      # bg: N, 512, H/32, W/32
      d5_f = self.decoder5_f(paddle.concat((bb, fea_list[-1]), 1))
      # d5_f: N, 256, H/16, W/16
      d4_f = self.decoder4_f(paddle.concat((d5_f, fea_list[-2]), 1))
      # d4_f: N, 128, H/8, W/8
      d3_f = self.decoder3_f(paddle.concat((d4_f, fea_list[-3]), 1))
      # d3_f: N, 64, H/4, W/4
      d2_f = self.decoder2_f(paddle.concat((d3_f, fea_list[-4]), 1))
      # d2_f: N, 64, H/2, W/2
      d1_f = self.decoder1_f(paddle.concat((d2_f, fea_list[-5]), 1))
      # d1_f: N, 64, H, W
      d0_f = self.decoder0_f(d1_f)
      # d0_f: N, 1, H, W
      focus_sigmoid = F.sigmoid(d0_f[:, 0:1, :, :])
      pha_sm = self.fusion(glance_sigmoid, focus_sigmoid)
      err_sm = d0_f[:, 1:2, :, :]
      err_sm = paddle.clip(err_sm, 0.0, 1.0)
      hid_sm = F.relu(d0_f[:, 2:, :, :])
  
      # Refiner
      if self.if_refine:
          pha = self.refiner(
              src=src,
              pha=pha_sm,
              err=err_sm,
              hid=hid_sm,
              tri=glance_sigmoid,
          )
          # Clamp outputs
          pha = paddle.clip(pha, 0.0, 1.0)
  
      if self.training:
          logit_dict = {
              "glance": glance_sigmoid,
              "focus": focus_sigmoid,
              "fusion": pha_sm,
              "error": err_sm,
          }
          if self.if_refine:
              logit_dict["refine"] = pha
          loss_dict = self.loss(logit_dict, data)
          return logit_dict, loss_dict
      else:
          return pha if self.if_refine else pha_sm
  
imports: from collections import defaultdict
  import time
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import paddleseg
  from paddleseg.models import layers
  from paddleseg import utils
  from paddleseg.cvlibs import manager
  from ppmatting.models.losses import MRSD
label_desc: Use image node_id 2 for calling the HumanMatting obj's underlying member method code with example usage: obj.forward(data) and returns: logit_dict,  loss_dict, unknown