class_docstr: Prints the count of each category of prediction that is present in the predictions.
  Can be useful to see, for example, to see if the model only gives one type of predictions,
  or if the predictions given are in the expected proportions 
class_docstr_tok: ["Prints","the","count","of","each","category","of","prediction","that","is","present","in","the","predictions",".","Can","be","useful","to","see",",","for","example",",","to","see","if","the","model","only","gives","one","type","of","predictions",",","or","if","the","predictions","given","are","in","the","expected","proportions"]
code: def __init__(self, inner_metric, name=None):
      super(Prediction_Counts, self).__init__(name)
      self.inner_metric = inner_metric
  
imports: from __future__ import division, print_function, absolute_import
  from .utils import get_from_module
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new Prediction_Counts object from inherited base classes: Metric with example: obj = Prediction_Counts(inner_metric, name)