class_docstr: This loss combines a Sigmoid layer and the BCELoss in one single class.
  
  The loss can be described as:
  
  .. math::
      p = \sigma(x)
  
  .. math::
      \ell(x, y) = mean(L) = mean(\{l_1,\dots,l_N\}^\top), \quad
      l_n = - \left [ y_n \cdot \log p_n + (1 - y_n) \cdot \log (1 - p_n) \right ],
  
  This is used for measuring the error of a reconstruction in for example an
  auto-encoder. Note that the targets t[i] should be numbers between 0 and 1.
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code: def forward(self, x, y):
      assert (
          x.size() == y.size()
      ), "input and target must have the same size"
      return x.binary_cross_entropy_with_logits(
          y, skip_forward=self.skip_forward
      )
  
imports: import crypten
  import torch
  from .module import Module
label_desc: Use image node_id 1 for calling the BCEWithLogitsLoss obj's underlying member method code with example usage: obj.forward(x, y) and returns: x