class_docstr: The SegNeXt implementation based on PaddlePaddle.
  
  The original article refers to
  Guo, Meng-Hao, et al. "SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation"
  (https://arxiv.org/pdf/2209.08575.pdf)
  
  Args:
      backbone (nn.Layer): The backbone must be an instance of MSCAN.
      decoder_cfg (dict): The arguments of decoder.
      num_classes (int): The unique number of target classes.
      backbone_indices (list(int), optional): The values indicate the indices of backbone output 
         used as the input of the SegNeXt head. Default: [1, 2, 3].
      pretrained (str, optional): The path or url of pretrained model. Default: None.
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code: def forward(self, x):
      input_size = paddle.shape(x)[2:]
      feats = self.backbone(x)
      out = self.decode_head(feats)
      return [
          F.interpolate(
              out,
              input_size,
              mode="bilinear",
              align_corners=self.align_corners,
          )
      ]
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models.backbones.mscan import MSCAN
  from paddleseg.models.layers import NMF2D, ConvGNAct
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the SegNeXt obj's underlying member method code with example usage: obj.forward(x) and returns: list