class_docstr: This code was developed by jbpatel@mit.edu.
      
class_docstr_tok: ["This","code","was","developed","by","jbpatel","@","mit.edu","."]
code: def metric_dice_dist(self, y_true, y_pred):
      from keras import backend as K
      from keras.layers import Lambda
  
      y_true1 = Lambda(
          (lambda arg: K.expand_dims(arg[..., 0], axis=-1))
      )(y_true)
      self.dice_coef(y_true1, y_pred)
      return self.diceMetric
  
imports: import numpy
  import itertools
label_desc: Use image node_id 8 for calling the WeightedCategoricalCrossEntropy obj's underlying member method code with example usage: obj.metric_dice_dist(y_true, y_pred) and returns: self