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"]
docstr: Prints the number of each kind of prediction 
docstr_tok: ["Prints","the","number","of","each","kind","of","prediction"]
code: def build(self, predictions, targets, inputs=None):
      """Prints the number of each kind of prediction"""
      self.built = True
      pshape = predictions.get_shape()
      self.inner_metric.build(predictions, targets, inputs)
  
      with tf.name_scope(self.name):
          if len(pshape) == 1 or (
              len(pshape) == 2 and int(pshape[1]) == 1
          ):
              self.name = self.name or "binary_prediction_counts"
              y, idx, count = tf.unique_with_counts(
                  tf.argmax(predictions)
              )
              self.tensor = tf.Print(
                  self.inner_metric,
                  [y, count],
                  name=self.inner_metric.name,
              )
          else:
              self.name = self.name or "categorical_prediction_counts"
              y, idx, count = tf.unique_with_counts(
                  tf.argmax(predictions, dimension=1)
              )
              self.tensor = tf.Print(
                  self.inner_metric.tensor,
                  [y, count],
                  name=self.inner_metric.name,
              )
  
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 2 for calling the Prediction_Counts obj's underlying member method code with example usage: obj.build(predictions, targets, inputs) without return types