code: def mean_absolute_error(preds: Tensor, target: Tensor) -> Tensor:
      """Compute mean absolute error.
  
      Args:
          preds: estimated labels
          target: ground truth labels
  
      Return:
          Tensor with MAE
  
      Example:
          >>> from torchmetrics.functional.regression import mean_absolute_error
          >>> x = torch.tensor([0., 1, 2, 3])
          >>> y = torch.tensor([0., 1, 2, 2])
          >>> mean_absolute_error(x, y)
          tensor(0.2500)
  
      """
      sum_abs_error, num_obs = _mean_absolute_error_update(
          preds, target
      )
      return _mean_absolute_error_compute(sum_abs_error, num_obs)
  
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 3 for calling a global function with example usage: mean_absolute_error(preds, target) and returns: _mean_absolute_error_compute