docstr: Adds training loss and scores for network.
docstr_tok: ["Adds","training","loss","and","scores","for","network","."]
code: def add_training_loss(self):
      """Adds training loss and scores for network."""
      pred, scores = self.get_scores()
      losses = tf.nn.sigmoid_cross_entropy_with_logits(
          logits=scores, labels=self.test_label_placeholder
      )
      weighted_losses = tf.multiply(
          losses, self.test_weight_placeholder
      )
      loss = tf.reduce_sum(weighted_losses)
  
      return pred, scores, loss
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import warnings
  import numpy
  import tensorflow
  import sys
  import time
  from deepchem.models import Model
  from deepchem.data import pad_batch
  from deepchem.data import NumpyDataset
  from deepchem.metrics import to_one_hot
  from deepchem.metrics import from_one_hot
  from deepchem.models.tf_new_models.graph_topology import merge_dicts
  from deepchem.nn import model_ops
  from deepchem.data import SupportGenerator
  from deepchem.data import EpisodeGenerator
  from deepchem.data import get_task_dataset
  from deepchem.data import get_single_task_test
  from deepchem.data import get_task_dataset_minus_support
  from deepchem.nn.copy import Input
label_desc: Use image node_id 7 for calling the SupportGraphClassifier obj's underlying member method code with example usage: obj.add_training_loss() and returns: pred,  scores,  loss