class_docstr: Semantic segmentation model with a light R-ASPP head.
  
  The original article refers to
      Howard, Andrew, et al. "Searching for mobilenetv3."
      (https://arxiv.org/pdf/1909.11065.pdf)
  
  Args:
      num_classes (int): The number of target classes.
      backbone(nn.Layer): Backbone network, such as stdc1net and resnet18. The backbone must
          has feat_channels, of which the length is 5.
      backbone_indices (List(int), optional): The values indicate the indices of backbone output 
          used as the input of the LR-ASPP head.
          Default: [0, 1, 3].
      lraspp_head_inter_chs (List(int), optional): The intermediate channels of LR-ASPP head.
          Default: [32, 64].
      lraspp_head_out_ch (int, optional): The output channels of each ASPP branch in the LR-ASPP head.
          Default: 128
      resize_mode (str, optional): The resize mode for the upsampling operation in the LR-ASPP head.
          Default: bilinear.
      use_gap (bool, optional): If true, use global average pooling in the LR-ASPP head; otherwise, use
          a 49x49 kernel for average pooling.
          Default: True.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
code: def forward(self, x):
      x_hw = paddle.shape(x)[2:]
  
      feats_backbone = self.backbone(x)
      assert len(feats_backbone) >= len(self.backbone_indices), (
          f"The nums of backbone feats ({len(feats_backbone)}) should be greater or "
          f"equal than the nums of backbone_indices ({len(self.backbone_indices)})"
      )
  
      y = self.lraspp_head(feats_backbone)
      y = F.interpolate(y, x_hw, mode="bilinear", align_corners=False)
      logit_list = [y]
  
      return logit_list
  
file_path: PaddleSeg/paddleseg/models/lraspp.py
imports: from functools import partial
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg import utils
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 2 for calling the LRASPP obj's underlying member method code with example usage: obj.forward(x) and returns: logit_list