code: def __call__(self, inputs):
      (label,) = tf_euler.get_dense_feature(
          inputs, [self.label_idx], [self.label_dim]
      )
      embedding = self.embed(inputs)
      logit = self.out_fc(embedding)
  
      metric = self.metric_class(label, tf.nn.sigmoid(logit))
      loss = tf.nn.sigmoid_cross_entropy_with_logits(
          labels=label, logits=logit
      )
      loss = tf.reduce_mean(loss)
      return (embedding, loss, self.metric_name, metric)
  
file_path: euler/tf_euler/python/mp_utils/base.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  import tf_euler
label_desc: Use image node_id 3 for calling the SuperviseModel obj's underlying member method code with example usage: obj.__call__(inputs) and returns: embedding,  loss,  self,  metric