class_docstr: EPGM class with converter methods to edgelist, adjacency matrix, etc.
class_docstr_tok: ["EPGM","class","with","converter","methods","to","edgelist",",","adjacency","matrix",",","etc","."]
docstr: Save the graph in GraphML XML format (e.g., for visualisation in gephi)
  Args:
      graph_id: unique id of the graph
      fname: file name to save the graphml into
  
  Returns:
      Exit code of nx.write_graphml()
docstr_tok: ["Save","the","graph","in","GraphML","XML","format","(","e.g.",",","for","visualisation","in","gephi",")","Args",":","graph_id",":","unique","id","of","the","graph","fname",":","file","name","to","save","the","graphml","into","Returns",":","Exit","code","of","nx.write_graphml","(",")"]
code: def save_as_graphml(self, graph_id, fname, directed):
      """
      Save the graph in GraphML XML format (e.g., for visualisation in gephi)
      Args:
          graph_id: unique id of the graph
          fname: file name to save the graphml into
  
      Returns:
          Exit code of nx.write_graphml()
  
      """
      if not any([graph_id in g["id"] for g in self.G["graphs"]]):
          raise Exception(
              "Graph with id {} does not exist".format(graph_id)
          )
  
      return nx.write_graphml(
          nx.DiGraph(self.to_nx(graph_id, directed)), path=fname
      )
  
imports: from collections import OrderedDict
  import networkx
  from networkx.readwrite import json_graph
  import os
  import json
  import uuid
  import numpy
  import scipy.sparse
  import multiprocessing
  from multiprocessing import Pool
  from functools import partial
  from time import sleep
label_desc: Use image node_id 17 for calling the EPGM obj's underlying member method code with example usage: obj.save_as_graphml(graph_id, fname, directed) and returns: nx