class_docstr: This minibatch iterator iterates over nodes for supervised learning.
  
  G -- networkx graph
  id2idx -- dict mapping node ids to integer values indexing feature tensor
  placeholders -- standard tensorflow placeholders object for feeding
  label_map -- map from node ids to class values (integer or list)
  num_classes -- number of output classes
  batch_size -- size of the minibatches
  max_degree -- maximum size of the downsampled adjacency lists
class_docstr_tok: ["This","minibatch","iterator","iterates","over","nodes","for","supervised","learning",".","G","--","networkx","graph","id2idx","--","dict","mapping","node","ids","to","integer","values","indexing","feature","tensor","placeholders","--","standard","tensorflow","placeholders","object","for","feeding","label_map","--","map","from","node","ids","to","class","values","(","integer","or","list",")","num_classes","--","number","of","output","classes","batch_size","--","size","of","the","minibatches","max_degree","--","maximum","size","of","the","downsampled","adjacency","lists"]
code: def node_val_feed_dict(self, size=None, test=False):
      if test:
          val_nodes = self.test_nodes
      else:
          val_nodes = self.val_nodes
      if not size is None:
          val_nodes = np.random.choice(val_nodes, size, replace=True)
      # add a dummy neighbor
      ret_val = self.batch_feed_dict(val_nodes)
      return ret_val[0], ret_val[1]
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
label_desc: Use image node_id 7 for calling the NodeMinibatchIterator obj's underlying member method code with example usage: obj.node_val_feed_dict(size, test) and returns: ret_val,  ret_val