class_docstr: Calculates the standard softmax ratio between anchor, positive and negative
  examples in a triplet based on euclidean distance.
class_docstr_tok: ["Calculates","the","standard","softmax","ratio","between","anchor",",","positive","and","negative","examples","in","a","triplet","based","on","euclidean","distance","."]
code: def __call__(self, y_true, y_pred):
      anchor, positive, negative = tf.unstack(y_pred)
      anchor_positive_distance = euclidean_distance(anchor, positive)
      anchor_negative_distance = euclidean_distance(anchor, negative)
  
      softmax = K.softmax(
          K.concatenate(
              [anchor_positive_distance, anchor_negative_distance]
          )
      )
      ideal_distance = K.constant([0.0, 1.0])
      return K.mean(K.abs(ideal_distance - softmax))
  
imports: import functools
  from tensorflow.keras import backend
  import tensorflow
  from tensorflow import keras
  import numpy
  from .distances import euclidean_distance, manhattan_distance, chebyshev_distance, cosine_distance
label_desc: Use image node_id 2 for calling the EuclideanSoftmaxRatioLoss obj's underlying member method code with example usage: obj.__call__(y_true, y_pred) and returns: K