class_docstr: The SegFormer implementation based on PaddlePaddle.
  
  The original article refers to
  Xie, Enze, et al. "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers." arXiv preprint arXiv:2105.15203 (2021).
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (Paddle.nn.Layer): A backbone network.
      embedding_dim (int): The MLP decoder channel dimension.
      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):
      feats = self.backbone(x)
      c1, c2, c3, c4 = feats
  
      ############## MLP decoder on C1-C4 ###########
      c1_shape = paddle.shape(c1)
      c2_shape = paddle.shape(c2)
      c3_shape = paddle.shape(c3)
      c4_shape = paddle.shape(c4)
  
      _c4 = (
          self.linear_c4(c4)
          .transpose([0, 2, 1])
          .reshape([0, 0, c4_shape[2], c4_shape[3]])
      )
      _c4 = F.interpolate(
          _c4,
          size=c1_shape[2:],
          mode="bilinear",
          align_corners=self.align_corners,
      )
  
      _c3 = (
          self.linear_c3(c3)
          .transpose([0, 2, 1])
          .reshape([0, 0, c3_shape[2], c3_shape[3]])
      )
      _c3 = F.interpolate(
          _c3,
          size=c1_shape[2:],
          mode="bilinear",
          align_corners=self.align_corners,
      )
  
      _c2 = (
          self.linear_c2(c2)
          .transpose([0, 2, 1])
          .reshape([0, 0, c2_shape[2], c2_shape[3]])
      )
      _c2 = F.interpolate(
          _c2,
          size=c1_shape[2:],
          mode="bilinear",
          align_corners=self.align_corners,
      )
  
      _c1 = (
          self.linear_c1(c1)
          .transpose([0, 2, 1])
          .reshape([0, 0, c1_shape[2], c1_shape[3]])
      )
  
      _c = self.linear_fuse(paddle.concat([_c4, _c3, _c2, _c1], axis=1))
  
      logit = self.dropout(_c)
      logit = self.linear_pred(logit)
      return [
          F.interpolate(
              logit,
              size=paddle.shape(x)[2:],
              mode="bilinear",
              align_corners=self.align_corners,
          )
      ]
  
file_path: PaddleSeg/paddleseg/models/segformer.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the SegFormer obj's underlying member method code with example usage: obj.forward(x) and returns: list