updated example, conversion of graphs

  >>>       adjacency_tensor = one_hot(batch[0], gan.edges)

  >>>       node_tensor = one_hot(batch[1], gan.nodes)

  >>>       yield {gan.data_inputs[0]: adjacency_tensor, gan.data_inputs[1]:node_tensor}

  >>> gan.fit_gan(iterbatches(10), generator_steps=0.2, checkpoint_interval=5000)

  >>> gan.fit_gan(iterbatches(8), generator_steps=0.2, checkpoint_interval=5000)

  >>> generated_data = gan.predict_gan_generator(1000)

  >>> # convert graphs to RDKitmolecules

  >>> nmols = feat.defeaturize(g)

  >>> print("{} molecules generated".format(len(nmols)))

  >>> # remove invalid moles

  >>> nmols = list(filter(lambda x: x is not None, nmols))

  >>> # currently training is unstable so 0 is a common outcome

  >>> print ("{} valid molecules".format(len(nmols)))

  References

  ----------