class_docstr: Implementation of EMANet (ICCV 2019 Oral).
code: def forward(self, img, lbl=None, size=None):
      x = self.extractor(img)
      x = self.fc0(x)
      x, mu = self.emau(x)
      x = self.fc1(x)
      x = self.fc2(x)
  
      if size is None:
          size = img.size()[-2:]
      pred = F.interpolate(
          x, size=size, mode="bilinear", align_corners=True
      )
  
      if self.training and lbl is not None:
          loss = self.crit(pred, lbl)
          return loss, mu
      else:
          return pred
  
imports: import math
  import torch.utils.model_zoo
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.modules.batchnorm import _BatchNorm
  from .None import settings
label_desc: Use image node_id 2 for calling the EMANet obj's underlying member method code with example usage: obj.forward(img, lbl, size) and returns: loss,  mu, pred