code: def construct_placeholders():
      # Define placeholders
      placeholders = {
          "batch1": tf.placeholder(
              tf.int32, shape=(None), name="batch1"
          ),
          "batch2": tf.placeholder(
              tf.int32, shape=(None), name="batch2"
          ),
          # negative samples for all nodes in the batch
          "neg_samples": tf.placeholder(
              tf.int32, shape=(None,), name="neg_sample_size"
          ),
          "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/unsupervised_train.py
imports: from __future__ import division
  from __future__ import print_function
  import os
  import time
  import tensorflow
  import numpy
  from graphsage.models import SampleAndAggregate, SAGEInfo, Node2VecModel
  from graphsage.minibatch import EdgeMinibatchIterator
  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() and returns: placeholders