code: def fmeasure(output, target, beta=1):
      """
      This operation computes the f-measure between the output and target. If beta is set as one,
      its called the f1-scorce or dice similarity coefficient. f1-scorce is monotonic in jaccard distance.
  
      f-measure = (1 + beta ** 2) * precision * recall / (beta ** 2 * precision + recall)
  
      This loss function is frequently used in semantic segmentation of images. Works with imbalanced classes, for
      balanced classes you should prefer cross_entropy instead.
      This operation works with both binary and multiclass classification.
  
      Args:
          output: the output values from the network
          target: it is usually a one-hot vector where the hot bit corresponds to the label index
          beta: greater than one weights recall higher than precision, less than one for the opposite.
          Commonly chosen values are 0.5, 1 or 2.
  
      Returns:
          :class:`~cntk.ops.functions.Function`
  
      """
  
      assert len(target.shape) == len(output.shape)
  
      if len(output.shape) == 3:
          axis = (1, 2)  # assumes that the first axis is the class axis
      else:
          axis = None
  
      correct_predictions = C.reduce_sum(output * target, axis=axis)
      precision = correct_predictions / C.reduce_sum(output, axis=axis)
      recall = correct_predictions / C.reduce_sum(target, axis=axis)
      return 1 - (1 + beta**2) * precision * recall / (
          beta**2 * precision + recall
      )
  
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 11 for calling a global function with example usage: fmeasure(output, target, beta) and returns: unknown