class_docstr: Creates a criterion that measures the mean absolute error between each element in
  the prediction :math:`x` and target :math:`y`.
  
  The loss can be described as:
  
  .. math::
      \ell(x, y) = mean(L) = mean(\{l_1,\dots,l_N\}^\top), \quad
      l_n = \left | x_n - y_n \right |,
  
  where :math:`N` is the batch size, :math:`x` and :math:`y` are tensors of
  arbitrary shapes with a total of :math:`n` elements each.
class_docstr_tok: ["Creates","a","criterion","that","measures","the","mean","absolute","error","between","each","element","in","the","prediction",":","math",":","`","x","`","and","target",":","math",":","`","y","`",".","The","loss","can","be","described","as",":","..","math",":",":","\\ell","(","x",",","y",")","=","mean","(","L",")","=","mean","(","\\","{","l_1",",","\\dots",",","l_N\\","}","^\\top",")",",","\\quad","l_n","=","\\left","|","x_n","-","y_n","\\right","|",",","where",":","math",":","`","N","`","is","the","batch","size",",",":","math",":","`","x","`","and",":","math",":","`","y","`","are","tensors","of","arbitrary","shapes","with","a","total","of",":","math",":","`","n","`","elements","each","."]
code: def forward(self, x, y):
      assert (
          x.size() == y.size()
      ), "input and target must have the same size"
      return (x - y).abs().mean()
  
imports: import crypten
  import torch
  from .module import Module
label_desc: Use image node_id 1 for calling the L1Loss obj's underlying member method code with example usage: obj.forward(x, y) and returns: unknown