class_docstr: This metric computes the accuracy using a similar interface to
  :class:`~torch.nn.BCEWithLogitsLoss`.
  
  Args:
      threshold (float): the threshold for class :math:`1`. Default value is ``0.``, that is a
          probability of ``sigmoid(0.) = 0.5``.
      reduction (string, optional): Specifies the reduction to apply to the output:
          ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
          ``'mean'``: the sum of the output will be divided by the number of
          elements in the output, ``'sum'``: the output will be summed.
  
  Possible string name:
      - ``'bin_acc'``
      - ``'binary_acc'``
      - ``'binary_accuracy'``
  
  Keys in :class:`logs<poutyne.Callback>` dictionary of callbacks:
      - Train: ``'bin_acc'``
      - Validation: ``'val_bin_acc'``
  
  Shape:
      - Input: :math:`(N, *)` where :math:`*` means, any number of additional
        dimensions
      - Target: :math:`(N, *)`, same shape as the input
      - Output: The binary accuracy.
code: def forward(self, y_pred, y_true):
      return bin_acc(
          y_pred,
          y_true,
          threshold=self.threshold,
          reduction=self.reduction,
      )
  
imports: from abc import ABC
  import torch.nn
  from poutyne.framework.metrics.metrics_registering import do_register_metric_func, register_metric_func
label_desc: Use image node_id 2 for calling the BinaryAccuracy obj's underlying member method code with example usage: obj.forward(y_pred, y_true) and returns: bin_acc