docstr: MAE loss as defined in: https://en.wikipedia.org/wiki/Mean_absolute_error.
  
  Given a time series of actual values :math:`y_t` and a time series of predicted values :math:`\hat{y}_t`
  both of length :math:`T`, it is computed as
  
  .. math::
      \frac{1}{T}
      \sum_{t=1}^{T}{\left| y_t - \hat{y}_t \right|}.
  
  Note that this is the same as torch.nn.L1Loss.
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code: def __init__(self):
      """
      MAE loss as defined in: https://en.wikipedia.org/wiki/Mean_absolute_error.
  
      Given a time series of actual values :math:`y_t` and a time series of predicted values :math:`\\hat{y}_t`
      both of length :math:`T`, it is computed as
  
      .. math::
          \\frac{1}{T}
          \\sum_{t=1}^{T}{\\left| y_t - \\hat{y}_t \\right|}.
  
      Note that this is the same as torch.nn.L1Loss.
      """
      super().__init__()
  
file_path: darts/darts/utils/losses.py
imports: import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new MAELoss object from inherited base classes: nn with example: obj = MAELoss()