code: def cross_entropy_with_softmax(
      output_vector, target_vector, axis=-1, name=""
  ):
      r"""
      This operation computes the cross entropy between the ``target_vector`` and
      the softmax of the ``output_vector``. The elements of ``target_vector``
      have to be non-negative and should sum to 1. The ``output_vector`` can
      contain any values. The function will internally compute the softmax of
      the ``output_vector``. Concretely,
  
      :math:`\mathrm{softmax}(x)=\left[\frac{\exp(x_1)}{\sum_i\exp(x_i)}\quad\frac{\exp(x_1)}{\sum_i\exp(x_i)}\quad\ldots\quad\frac{\exp(x_1)}{\sum_i\exp(x_i)}\right]`
  
      :math:`\mathrm{cross\_entropy\_with\_softmax}(o, t) = -\sum_{i} t_i \log(\mathrm{softmax}(o)_i)`
  
      with the understanding that the implementation can use equivalent formulas
      for efficiency and numerical stability.
  
      Example:
          >>> C.cross_entropy_with_softmax([[1., 1., 1., 50.]], [[0., 0., 0., 1.]]).eval()
          array([[ 0.]], dtype=float32)
  
          >>> C.cross_entropy_with_softmax([[1., 2., 3., 4.]], [[0.35, 0.15, 0.05, 0.45]]).eval()
          array([[ 1.84019]], dtype=float32)
  
      Args:
          output_vector: the unscaled computed output values from the network
          target_vector: usually it is one-hot vector where the hot bit
           corresponds to the label index. But it can be any probability
           distribution over the labels.
          axis (int or :class:`~cntk.axis.Axis`, optional): if given, cross entropy will be computed
                  along this axis
          name (str, optional): the name of the Function instance in the network
      Returns:
          :class:`~cntk.ops.functions.Function`
      """
      from cntk.cntk_py import cross_entropy_with_softmax
  
      dtype = get_data_type(output_vector, target_vector)
      output_vector = sanitize_input(output_vector, dtype)
      target_vector = sanitize_input(target_vector, dtype)
      axis = sanitize_axis(axis)
      return cross_entropy_with_softmax(
          output_vector, target_vector, axis, 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 5 for calling a global function with example usage: cross_entropy_with_softmax(output_vector, target_vector, axis, name) and returns: cross_entropy_with_softmax