be equal to \frac{300}{100}=3. The loss would act as if the dataset
   contains 3\times 100=300 positive examples.
Examples:
  >>> target = torch.ones([10, 64], dtype=torch.float32)  # 64 classes, batch size = 10
  >>> output = torch.full([10, 64], 1.5)  # A prediction (logit)
  >>> pos_weight = torch.ones([64])  # All weights are equal to 1
  >>> criterion = torch.nn.BCEWithLogitsLoss(pos_weight=pos_weight)
  >>> criterion(output, target)  # -log(sigmoid(1.5))
  tensor(0.20...)

Parameters:
      * weight (Tensor, optional) -- a manual rescaling
        weight given to the loss of each batch element. If given, has
        to be a Tensor of size nbatch.
  * **size_average** (*bool**, **optional*) -- Deprecated (see
    "reduction"). By default, the losses are averaged over each
    loss element in the batch. Note that for some losses, there
    are multiple elements per sample. If the field "size_average"
