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 batch_feed_dict(self, batch_nodes, val=False):
      batch1id = batch_nodes
      batch1 = [self.id2idx[n] for n in batch1id]
  
      labels = np.vstack(
          [self._make_label_vec(node) for node in batch1id]
      )
      feed_dict = dict()
      feed_dict.update({self.placeholders["batch_size"]: len(batch1)})
      feed_dict.update({self.placeholders["batch"]: batch1})
      feed_dict.update({self.placeholders["labels"]: labels})
  
      return feed_dict, labels
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
label_desc: Use image node_id 6 for calling the NodeMinibatchIterator obj's underlying member method code with example usage: obj.batch_feed_dict(batch_nodes, val) and returns: feed_dict,  labels