code: def __call__(self, inputs):
      inputs = self.parse_input_fn(inputs)
      label = inputs[0]
      if len(inputs) == 2:
          node_groups = inputs[1]
      else:
          node_groups = inputs[1:]
  
      if self.neg_sample_fn is not None:
          neg_node_groups = self.neg_sample_fn(node_groups)
          neg_labels = tf.zeros_like(
              neg_node_groups[0], dtype=tf.float32
          )
          label = tf.concat([label, neg_labels], axis=0)
          pos_node_groups = node_groups
          node_groups = []
          for pos_nodes, neg_nodes in zip(
              pos_node_groups, neg_node_groups
          ):
              node_groups.append(
                  tf.concat([pos_node_groups, neg_node_groups], axis=0)
              )
  
      node_groups_embedding = self.embed(node_groups)
      (
          target_embedding,
          context_embedding,
          output_embedding,
      ) = self.parse_group_emb_fn(node_groups_embedding)
      logit = self.logit_fn(
          target_embedding, context_emb=context_embedding
      )
      loss = self.loss_fn(label, logit)
      metric = self.metric_class(label, logit)
      embedding = output_embedding
      return (embedding, loss, self.metric_name, metric)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  import tf_euler
  import six
  from tf_euler.python.solution.logits import CosineLogits, PosNegLogits
  from tf_euler.python.solution.losses import sigmoid_loss, xent_loss
  from tf_euler.python.solution.base_unsupervise import UnsuperviseSolution
label_desc: Use image node_id 3 for calling the SuperviseSampleSolution obj's underlying member method code with example usage: obj.__call__(inputs) and returns: embedding,  loss,  self,  metric