class_docstr: Creates a criterion that measures the Binary Cross Entropy
  between the prediction :math:`x` and the 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 [ y_n \cdot \log x_n + (1 - y_n) \cdot \log (1 - x_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.
  
  This is used for measuring the error of a reconstruction in for example
  an auto-encoder. Note that the targets :math:`y` 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(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 BCELoss obj's underlying member method code with example usage: obj.forward(x, y) and returns: x