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 _make_label_vec(self, node):
      label = self.label_map[node]
      if isinstance(label, list):
          label_vec = np.array(label)
      else:
          label_vec = np.zeros((self.num_classes))
          class_ind = self.label_map[node]
          label_vec[class_ind] = 1
      return label_vec
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
label_desc: Use image node_id 2 for calling the NodeMinibatchIterator obj's underlying member method code with example usage: obj._make_label_vec(node) and returns: label_vec