code: def _mean_absolute_error_update(
      preds: Tensor, target: Tensor
  ) -> Tuple[Tensor, int]:
      """Update and returns variables required to compute Mean Absolute Error.
  
      Check for same shape of input tensors.
  
      Args:
          preds: Predicted tensor
          target: Ground truth tensor
  
      """
      _check_same_shape(preds, target)
      preds = preds if preds.is_floating_point else preds.float()  # type: ignore[truthy-function] # todo
      target = target if target.is_floating_point else target.float()  # type: ignore[truthy-function] # todo
      sum_abs_error = torch.sum(torch.abs(preds - target))
      return sum_abs_error, target.numel()
  
imports: from typing import Tuple, Union
  import torch
  from torch import Tensor
  from torchmetrics.utilities.checks import _check_same_shape
label_desc: Use image node_id 1 for calling a global function with example usage: _mean_absolute_error_update(preds, target) and returns: sum_abs_error,  target