original_docstring: Given a batch of training data, update metric parameters.
  
      Args :
          triplet_batch : List [anchor, positive, negative], each with shape:
                          (batch x cells x time_window)
      Returns :
          loss : Training loss for the batch of data.
      
docstring: Given a batch of training data, update metric parameters.
docstring_tokens: Given a batch of training data update metric parameters .
code: def update(self, triplet_batch):
      """Given a batch of training data, update metric parameters.
  
      Args :
          triplet_batch : List [anchor, positive, negative], each with shape:
                          (batch x cells x time_window)
      Returns :
          loss : Training loss for the batch of data.
      """
      feed_dict = {self.anchor: triplet_batch[0],
                   self.pos: triplet_batch[1],
                   self.neg: triplet_batch[2]}
      _, loss = self.sess.run([self.train_step, self.loss], feed_dict=feed_dict)
      tf.logging.info(loss)
  
      return loss
code_tokens: def update ( self , triplet_batch ) : feed_dict = { self . anchor : triplet_batch [ 0 ] , self . pos : triplet_batch [ 1 ] , self . neg : triplet_batch [ 2 ] } _ , loss = self . sess . run ( [ self . train_step , self . loss ] , feed_dict = feed_dict ) tf . logging . info ( loss ) return loss
short_docstring: Given a batch of training data, update metric parameters.
short_docstring_tokens: Given a batch of training data update metric parameters .
comment: """Given a batch of training data, update metric parameters.
  
      Args :
          triplet_batch : List [anchor, positive, negative], each with shape:
                          (batch x cells x time_window)
      Returns :
          loss : Training loss for the batch of data.
      """