class_docstr: A model for 
class_docstr_tok: ["A","model","for"]
code: def loss(self, logit_dict, label_dict, loss_func_dict=None):
      if loss_func_dict is None:
          if self.loss_func_dict is None:
              self.loss_func_dict = defaultdict(list)
              self.loss_func_dict["glance"].append(nn.NLLLoss())
              self.loss_func_dict["focus"].append(MRSD())
              self.loss_func_dict["cm"].append(MRSD())
              self.loss_func_dict["err"].append(
                  paddleseg.models.MSELoss()
              )
              self.loss_func_dict["refine"].append(
                  paddleseg.models.L1Loss()
              )
      else:
          self.loss_func_dict = loss_func_dict
  
      loss = {}
  
      # glance loss computation
      # get glance label
      glance_label = F.interpolate(
          label_dict["trimap"],
          logit_dict["glance"].shape[2:],
          mode="nearest",
          align_corners=False,
      )
      glance_label_trans = (glance_label == 128).astype("int64")
      glance_label_bg = (glance_label == 0).astype("int64")
      glance_label = glance_label_trans + glance_label_bg * 2
      loss_glance = self.loss_func_dict["glance"][0](
          paddle.log(logit_dict["glance"] + 1e-6),
          glance_label.squeeze(1),
      )
      loss["glance"] = loss_glance
  
      # focus loss computation
      focus_label = F.interpolate(
          label_dict["alpha"],
          logit_dict["focus"].shape[2:],
          mode="bilinear",
          align_corners=False,
      )
      loss_focus = self.loss_func_dict["focus"][0](
          logit_dict["focus"], focus_label, glance_label_trans
      )
      loss["focus"] = loss_focus
  
      # collaborative matting loss
      loss_cm_func = self.loss_func_dict["cm"]
      # fusion_sigmoid loss
      loss_cm = loss_cm_func[0](logit_dict["fusion"], focus_label)
      loss["cm"] = loss_cm
  
      # error loss
      err = F.interpolate(
          logit_dict["error"],
          label_dict["alpha"].shape[2:],
          mode="bilinear",
          align_corners=False,
      )
      err_label = (
          F.interpolate(
              logit_dict["fusion"],
              label_dict["alpha"].shape[2:],
              mode="bilinear",
              align_corners=False,
          )
          - label_dict["alpha"]
      ).abs()
      loss_err = self.loss_func_dict["err"][0](err, err_label)
      loss["err"] = loss_err
  
      loss_all = (
          0.25 * loss_glance
          + 0.25 * loss_focus
          + 0.25 * loss_cm
          + loss_err
      )
  
      # refine loss
      if self.if_refine:
          loss_refine = self.loss_func_dict["refine"][0](
              logit_dict["refine"], label_dict["alpha"]
          )
          loss["refine"] = loss_refine
          loss_all = loss_all + loss_refine
  
      loss["all"] = loss_all
      return loss
  
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 3 for calling the HumanMatting obj's underlying member method code with example usage: obj.loss(logit_dict, label_dict, loss_func_dict) and returns: loss