code: def feature_mean_frac(bin, bin_count, image_name=IMAGE_NAME):
      if bin == bin_count + 1:
          return "_".join(
              [
                  cellprofiler.modules.measureobjectintensitydistribution.M_CATEGORY,
                  cellprofiler.modules.measureobjectintensitydistribution.F_MEAN_FRAC,
                  image_name,
                  cellprofiler.modules.measureobjectintensitydistribution.FF_OVERFLOW,
              ]
          )
  
      return (
          cellprofiler.modules.measureobjectintensitydistribution.M_CATEGORY
          + "_"
          + cellprofiler.modules.measureobjectintensitydistribution.FF_MEAN_FRAC
          % (image_name, bin, bin_count)
      )
  
imports: import io
  import centrosome.propagate
  import numpy
  import scipy.stats
  import cellprofiler_core.image
  import cellprofiler_core.measurement
  import cellprofiler_core.module
  from cellprofiler_core.constants.measurement import COLTYPE_FLOAT
  import cellprofiler.modules.measureobjectintensitydistribution
  import cellprofiler_core.object
  import cellprofiler_core.pipeline
  import cellprofiler_core.preferences
  import cellprofiler_core.setting
  import cellprofiler_core.workspace
  import tests.frontend.modules
label_desc: Use image node_id 2 for calling a global function with example usage: feature_mean_frac(bin, bin_count, image_name) and returns: unknown, str