code: def predict(self, sample_latent=None, batch_size=1):
      self.init_sess()
  
      if sample_latent is None:
          sample_latent = np.random.normal(
              size=[batch_size, self.latent_size]
          )
  
      return self.sess.run(
          self.synthetic_images, feed_dict={self.latent: sample_latent}
      )
  
file_path: DeepNeuro/deepneuro/models/gan.py
imports: import numpy
  import tensorflow
  import os
  from deepneuro.models.tensorflow_model import TensorFlowModel
  from deepneuro.utilities.util import add_parameter
  from deepneuro.models.blocks import generator, discriminator
  from deepneuro.models.cost_functions import wasserstein_loss
label_desc: Use image node_id 7 for calling the GAN obj's underlying member method code with example usage: obj.predict(sample_latent, batch_size) and returns: self