code: def construct_placeholders(num_classes):
      # Define placeholders
      placeholders = {
          "labels": tf.placeholder(
              tf.float32, shape=(None, num_classes), name="labels"
          ),
          "batch": tf.placeholder(
              tf.int32, shape=(None), name="batch1"
          ),
          "dropout": tf.placeholder_with_default(
              0.0, shape=(), name="dropout"
          ),
          "batch_size": tf.placeholder(tf.int32, name="batch_size"),
      }
      return placeholders
  
file_path: GraphSAGE/graphsage/supervised_train.py
imports: from __future__ import division
  from __future__ import print_function
  import os
  import time
  import tensorflow
  import numpy
  import sklearn
  from sklearn import metrics
  from graphsage.supervised_models import SupervisedGraphsage
  from graphsage.models import SAGEInfo
  from graphsage.minibatch import NodeMinibatchIterator
  from graphsage.neigh_samplers import UniformNeighborSampler
  from graphsage.utils import load_data
label_desc: Use image node_id 5 for calling a global function with example usage: construct_placeholders(num_classes) and returns: placeholders