code: def binary_cross_entropy(output, target, name=""):
      r"""
      Computes the binary cross entropy (aka logistic loss) between the ``output`` and ``target``.
  
      Args:
          output: the computed posterior probability for a variable to be 1 from the network (typ. a ``sigmoid``)
          target: ground-truth label, 0 or 1
          name (str, optional): the name of the Function instance in the network
  
      Returns:
          :class:`~cntk.ops.functions.Function`
  
      Todo:
          add an example
      """
      from cntk.cntk_py import binary_cross_entropy
  
      dtype = get_data_type(output, target)
      output = sanitize_input(output, dtype)
      target = sanitize_input(target, dtype)
      return binary_cross_entropy(output, target, name)
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  from ..ops.functions import CloneMethod, Function
  from ..variables import Variable, Parameter, Constant
  from cntk.internal import sanitize_input, sanitize_shape, sanitize_axis, sanitize_dynamic_axes, typemap
  from cntk.internal.utils import get_data_type
  from cntk.cntk_py import sentinel_value_for_auto_select_random_seed
  from cntk import times, softmax, parameter
  import cntk
  from ..axis import Axis
label_desc: Use image node_id 3 for calling a global function with example usage: binary_cross_entropy(output, target, name) and returns: binary_cross_entropy