class_docstr: Pyramid Scene Parsing Network
  
  Parameters
  ----------
  nclass : int
      Number of categories for the training dataset.
  backbone : string
      Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
      'resnet101' or 'resnet152').
  norm_layer : object
      Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
      for Synchronized Cross-GPU BachNormalization).
  aux : bool
      Auxiliary loss.
  Reference:
      Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu.
      "Dual Attention Network for Scene Segmentation." *CVPR*, 2019
code: def forward(self, x):
      size = x.size()[2:]
      _, _, c3, c4 = self.base_forward(x)
      outputs = []
      x = self.head(c4)
      x0 = F.interpolate(
          x[0], size, mode="bilinear", align_corners=True
      )
      outputs.append(x0)
  
      if self.aux and self.training:
          x1 = F.interpolate(
              x[1], size, mode="bilinear", align_corners=True
          )
          x2 = F.interpolate(
              x[2], size, mode="bilinear", align_corners=True
          )
          outputs.append(x1)
          outputs.append(x2)
          return outputs
      else:
          return outputs[0]
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.segbase import SegBaseModel
label_desc: Use image node_id 2 for calling the DANet obj's underlying member method code with example usage: obj.forward(x) and returns: outputs, outputs