class_docstr: distributed nphi (n_topics by n_words), transpose to/from graph nodes data
class_docstr_tok: ["distributed","nphi","(","n_topics","by","n_words",")",",","transpose","to\/from","graph","nodes","data"]
code: def update_from(self, G, mult, rho):
      nphi = G.nodes["word"].data["nphi"].T * mult
  
      if "_ID" in G.nodes["word"].data:
          _ID = G.nodes["word"].data["_ID"]
      else:
          _ID = slice(None)
  
      mean_change = [
          x.update(y, _ID, rho)
          for x, y in zip(self, self.split_device(nphi))
      ]
      return np.mean(mean_change)
  
imports: import collections
  import functools
  import io
  import os
  import warnings
  import dgl
  import numpy
  import scipy
  import torch
label_desc: Use image node_id 2 for calling the WordData obj's underlying member method code with example usage: obj.update_from(G, mult, rho) and returns: np