class_docstr: Interlaced Sparse Self-Attention for Semantic Segmentation.
  
  The original article refers to Lang Huang, et al. "Interlaced Sparse Self-Attention for Semantic Segmentation"
  (https://arxiv.org/abs/1907.12273).
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone_indices (tuple): The values in the tuple indicate the indices of output of backbone.
      isa_channels (int): The channels of ISA Module.
      down_factor (tuple): Divide the height and width dimension to (Ph, PW) groups.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
      align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
          is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.  Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
class_docstr_tok: ["Interlaced","Sparse","Self-Attention","for","Semantic","Segmentation",".","The","original","article","refers","to","Lang","Huang",",","et","al",".","``","Interlaced","Sparse","Self-Attention","for","Semantic","Segmentation","''","(","https",":","\/\/arxiv.org\/abs\/1907.12273",")",".","Args",":","num_classes","(","int",")",":","The","unique","number","of","target","classes",".","backbone_indices","(","tuple",")",":","The","values","in","the","tuple","indicate","the","indices","of","output","of","backbone",".","isa_channels","(","int",")",":","The","channels","of","ISA","Module",".","down_factor","(","tuple",")",":","Divide","the","height","and","width","dimension","to","(","Ph",",","PW",")","groups",".","enable_auxiliary_loss","(","bool",",","optional",")",":","A","bool","value","indicates","whether","adding","auxiliary","loss",".","Default",":","True",".","align_corners","(","bool",")",":","An","argument","of","F.interpolate",".","It","should","be","set","to","False","when","the","output","size","of","feature","is","even",",","e.g",".","1024x512",",","otherwise","it","is","True",",","e.g",".","769x769",".","Default",":","False",".","pretrained","(","str",",","optional",")",":","The","path","or","url","of","pretrained","model",".","Default",":","None","."]
code: def forward(self, x: paddle.Tensor) -> List[paddle.Tensor]:
      feats = self.backbone(x)
      feats = [feats[i] for i in self.backbone_indices]
      logit_list = self.head(feats)
      logit_list = [
          F.interpolate(
              logit,
              paddle.shape(x)[2:],
              mode="bilinear",
              align_corners=self.align_corners,
              align_mode=1,
          )
          for logit in logit_list
      ]
  
      return logit_list
  
imports: import os
  from typing import Union, List, Tuple
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  from isanet_resnet50_voc.resnet import ResNet50_vd
  import isanet_resnet50_voc.layers
label_desc: Use image node_id 3 for calling the ISANet obj's underlying member method code with example usage: obj.forward(x) and returns: logit_list