code: def forward(
      self,
      imgs,
      labels=None,
  ):
      if (
          self.training
          and self.mixup_fn is not None
          and labels is not None
      ):
          imgs, labels = self.mixup_fn(imgs, labels)
  
      if self.linear_classifier:
          with torch.no_grad():
              x = self.model_forward(imgs)
      else:
          x = self.model_forward(imgs)
  
      if self.cfg.prediction_mode == PredictionMode.MEAN_POOLING:
          x = x.mean(dim=1)
      elif self.cfg.prediction_mode == PredictionMode.CLS_TOKEN:
          x = x[:, 0]
      elif self.cfg.prediction_mode == PredictionMode.LIN_SOFTMAX:
          dtype = x.dtype
          x = F.logsigmoid(x.float())
          x = torch.logsumexp(x + x, dim=1) - torch.logsumexp(
              x + 1e-6, dim=1
          )
          x = x.clamp(max=0)
          x = x - torch.log(-(torch.expm1(x)))
          x = torch.nan_to_num(x, nan=0, posinf=0, neginf=0)
          x = x.to(dtype=dtype)
      else:
          raise Exception(
              f"unknown prediction mode {self.cfg.prediction_mode.name}"
          )
  
      if self.fc_norm is not None:
          x = self.fc_norm(x)
  
      x = self.head(x)
  
      if labels is None:
          return x
  
      if self.training and self.mixup_fn is not None:
          loss = -labels * F.log_softmax(x.float(), dim=-1)
      else:
          loss = F.cross_entropy(
              x.float(),
              labels,
              label_smoothing=self.cfg.label_smoothing
              if self.training
              else 0,
              reduction="none",
          )
  
      result = {
          "losses": {"regression": loss},
          "sample_size": imgs.size(0),
      }
  
      if not self.training:
          with torch.no_grad():
              pred = x.argmax(-1)
              correct = (pred == labels).sum()
              result["correct"] = correct
  
      return result
  
imports: import logging
  from dataclasses import dataclass
  from enum import Enum, auto
  from typing import Any, Optional
  import numpy
  from omegaconf import II, MISSING
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import checkpoint_utils, tasks
  from omegaconf import open_dict
  from fairseq.dataclass import FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
  from .mae import interpolate_pos_embed
label_desc: Use image node_id 3 for calling the MaeImageClassificationModel obj's underlying member method code with example usage: obj.forward(imgs, labels) and returns: result, x