class_docstr: Uniformly samples neighbors.
  Assumes that adj lists are padded with random re-sampling
code: def _call(self, inputs):
      ids, num_samples = inputs
      adj_lists = tf.nn.embedding_lookup(self.adj_info, ids)
      adj_lists = tf.transpose(
          tf.random_shuffle(tf.transpose(adj_lists))
      )
      adj_lists = tf.slice(adj_lists, [0, 0], [-1, num_samples])
      return adj_lists
  
imports: from __future__ import division
  from __future__ import print_function
  from graphsage.layers import Layer
  import tensorflow
label_desc: Use image node_id 2 for calling the UniformNeighborSampler obj's underlying member method code with example usage: obj._call(inputs) and returns: adj_lists