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 __init__(
      self,
      G,
      id2idx,
      placeholders,
      label_map,
      num_classes,
      batch_size=100,
      max_degree=25,
      **kwargs,
  ):
      self.G = G
      self.nodes = G.nodes()
      self.id2idx = id2idx
      self.placeholders = placeholders
      self.batch_size = batch_size
      self.max_degree = max_degree
      self.batch_num = 0
      self.label_map = label_map
      self.num_classes = num_classes
  
      self.adj, self.deg = self.construct_adj()
      self.test_adj = self.construct_test_adj()
  
      self.val_nodes = [
          n for n in self.G.nodes() if self.G.node[n]["val"]
      ]
      self.test_nodes = [
          n for n in self.G.nodes() if self.G.node[n]["test"]
      ]
  
      self.no_train_nodes_set = set(self.val_nodes + self.test_nodes)
      self.train_nodes = set(G.nodes()).difference(
          self.no_train_nodes_set
      )
      # don't train on nodes that only have edges to test set
      self.train_nodes = [
          n for n in self.train_nodes if self.deg[id2idx[n]] > 0
      ]
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
label_desc: Use image node_id 1 to create a new NodeMinibatchIterator object from inherited base classes: object with example: obj = NodeMinibatchIterator(G, id2idx, placeholders, label_map, num_classes, batch_size, max_degree)