class_docstr: The ESPNetV2 implementation based on PaddlePaddle.
  
  The original article refers to
  Sachin Mehta, Mohammad Rastegari, Linda Shapiro, and Hannaneh Hajishirzi. "ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network"
  (https://arxiv.org/abs/1811.11431).
  
  Args:
      num_classes (int): The unique number of target classes.
      in_channels (int, optional): Number of input channels. Default: 3.
      scale (float, optional): The scale of channels, only support scale <= 1.5 and scale == 2. Default: 1.0.
      drop_prob (floa, optional): The probability of dropout. Default: 0.1.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
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code: def forward(self, x):
      out_l1, out_l2, out_l3, out_l4 = self.backbone(x)
  
      out_l4_proj = self.proj_l4_c(out_l4)
      l4_to_l3 = F.interpolate(
          out_l4_proj,
          scale_factor=2,
          mode="bilinear",
          align_corners=True,
      )
      merged_l3 = self.eesp_psp(
          paddle.concat([out_l3, l4_to_l3], axis=1)
      )
      proj_merge_l3 = self.project_l3(merged_l3)
      proj_merge_l3 = self.act_l3(proj_merge_l3)
  
      l3_to_l2 = F.interpolate(
          proj_merge_l3,
          scale_factor=2,
          mode="bilinear",
          align_corners=True,
      )
      merged_l2 = self.project_l2(
          paddle.concat([out_l2, l3_to_l2], axis=1)
      )
  
      l2_to_l1 = F.interpolate(
          merged_l2, scale_factor=2, mode="bilinear", align_corners=True
      )
      merged_l1 = self.project_l1(
          paddle.concat([out_l1, l2_to_l1], axis=1)
      )
  
      if self.training:
          return [
              F.interpolate(
                  merged_l1,
                  scale_factor=2,
                  mode="bilinear",
                  align_corners=True,
              ),
              self.hierarchical_upsample(proj_merge_l3),
          ]
      else:
          return [
              F.interpolate(
                  merged_l1,
                  scale_factor=2,
                  mode="bilinear",
                  align_corners=True,
              )
          ]
  
imports: import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg import utils
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
label_desc: Use image node_id 4 for calling the ESPNetV2 obj's underlying member method code with example usage: obj.forward(x) and returns: list, list