class_docstr: The method class for GraphAF algorithm proposed in the paper `GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation <https://arxiv.org/abs/2001.09382>`_. This class provides interfaces for running random generation, property
  optimization, and constrained optimization with GraphAF. Please refer to the `example codes <https://github.com/divelab/DIG/tree/dig-stable/examples/ggraph/GraphAF>`_ for usage examples.
class_docstr_tok: ["The","method","class","for","GraphAF","algorithm","proposed","in","the","paper","`","GraphAF",":","a","Flow-based","Autoregressive","Model","for","Molecular","Graph","Generation","<","https",":","\/\/arxiv.org\/abs\/2001.09382",">","`","_",".","This","class","provides","interfaces","for","running","random","generation",",","property","optimization",",","and","constrained","optimization","with","GraphAF",".","Please","refer","to","the","`","example","codes","<","https",":","\/\/github.com\/divelab\/DIG\/tree\/dig-stable\/examples\/ggraph\/GraphAF",">","`","_","for","usage","examples","."]
code: def __init__(self):
      super(GraphAF, self).__init__()
      self.model = None
  
file_path: DIG/dig/ggraph/method/GraphAF/graphaf.py
imports: import os
  import torch
  import torch.nn
  from rdkit import Chem
  from dig.ggraph.method import Generator
  from .model import GraphFlowModel, GraphFlowModel_rl, GraphFlowModel_con_rl
  from .train_utils import adjust_learning_rate, DataIterator
label_desc: Use image node_id 1 to create a new GraphAF object from inherited base classes: Generator with example: obj = GraphAF()