class_docstr: This minibatch iterator iterates over batches of sampled edges or
  random pairs of co-occuring edges.
  
  G -- networkx graph
  id2idx -- dict mapping node ids to index in feature tensor
  placeholders -- tensorflow placeholders object
  context_pairs -- if not none, then a list of co-occuring node pairs (from random walks)
  batch_size -- size of the minibatches
  max_degree -- maximum size of the downsampled adjacency lists
  n2v_retrain -- signals that the iterator is being used to add new embeddings to a n2v model
  fixed_n2v -- signals that the iterator is being used to retrain n2v with only existing nodes as context
class_docstr_tok: ["This","minibatch","iterator","iterates","over","batches","of","sampled","edges","or","random","pairs","of","co-occuring","edges",".","G","--","networkx","graph","id2idx","--","dict","mapping","node","ids","to","index","in","feature","tensor","placeholders","--","tensorflow","placeholders","object","context_pairs","--","if","not","none",",","then","a","list","of","co-occuring","node","pairs","(","from","random","walks",")","batch_size","--","size","of","the","minibatches","max_degree","--","maximum","size","of","the","downsampled","adjacency","lists","n2v_retrain","--","signals","that","the","iterator","is","being","used","to","add","new","embeddings","to","a","n2v","model","fixed_n2v","--","signals","that","the","iterator","is","being","used","to","retrain","n2v","with","only","existing","nodes","as","context"]
code: def label_val(self):
      train_edges = []
      val_edges = []
      for n1, n2 in self.G.edges():
          if (
              self.G.node[n1]["val"]
              or self.G.node[n1]["test"]
              or self.G.node[n2]["val"]
              or self.G.node[n2]["test"]
          ):
              val_edges.append((n1, n2))
          else:
              train_edges.append((n1, n2))
      return train_edges, val_edges
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
label_desc: Use image node_id 13 for calling the EdgeMinibatchIterator obj's underlying member method code with example usage: obj.label_val() and returns: train_edges,  val_edges