L1Loss
class torch.nn.L1Loss(size_average=None, reduce=None, reduction='mean')
Creates a criterion that measures the mean absolute error (MAE)
   between each element in the input x and target y.
The unreduced (i.e. with "reduction" set to "'none'") loss can be
   described as:
  \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad l_n = \left| x_n
  - y_n \right|,

where N is the batch size. If "reduction" is not "'none'" (default
   "'mean'"), then:
  \ell(x, y) = \begin{cases}     \operatorname{mean}(L), &
  \text{if reduction} = \text{`mean';}\\
  \operatorname{sum}(L),  & \text{if reduction} = \text{`sum'.}
  \end{cases}

x and y are tensors of arbitrary shapes with a total of n elements
   each.
The sum operation still operates over all the elements, and divides
   by n.
The division by n can be avoided if one sets "reduction = 'sum'".
Supports real-valued and complex-valued inputs.
Parameters: