code: def update(self, pred: torch.Tensor, gt: torch.Tensor):
      batch_size, c, h, w = gt.shape
      assert (
          batch_size == 1
      ), f"validation mode batch_size must be 1, but got batch_size: {batch_size}."
      resize_pred = F.interpolate(
          pred, (h, w), mode="bilinear", align_corners=False
      )
      error_pixels = torch.sum(
          torch.abs(resize_pred - gt), dim=(1, 2, 3)
      ) / (h * w)
      self.mae_list.extend(error_pixels.tolist())
  
imports: from collections import defaultdict, deque
  import datetime
  import time
  import torch
  import torch.distributed
  import torch.nn.functional
  import errno
  import os
label_desc: Use image node_id 2 for calling the MeanAbsoluteError obj's underlying member method code with example usage: obj.update(pred, gt) without return types