MSELoss
class torch.nn.MSELoss(size_average=None, reduce=None, reduction='mean')
Creates a criterion that measures the mean squared error (squared
   L2 norm) 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)^2,

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 mean operation still operates over all the elements, and
   divides by n.
The division by n can be avoided if one sets "reduction = 'sum'".
Parameters:
      * size_average (bool, optional) -- Deprecated (see