code: def process_step(self):
      for epoch in range(self.num_epochs):
          step_counter = tqdm(
              list(range(self.training_steps_per_epoch)),
              total=self.training_steps_per_epoch,
              unit="step",
              desc="Generator Loss:",
              miniters=1,
          )
  
          for step in step_counter:
              # Replace with GPU function?
              sample_latent = np.random.normal(
                  size=[self.training_batch_size, self.latent_size]
              )
              reference_data = next(self.training_data_generator)[
                  self.input_data
              ]
  
              # Optimize!
  
              _, g_loss = self.sess.run(
                  [self.basic_optimizer, self.basic_loss],
                  feed_dict={
                      self.reference_images: reference_data,
                      self.latent: sample_latent,
                  },
              )
  
              self.log(
                  [g_loss],
                  headers=["Basic Loss"],
                  verbose=self.hyperverbose,
              )
              step_counter.set_description(
                  "Generator Loss: {0:.5f}".format(g_loss)
              )
  
          self.save_model(self.output_model_filepath)
  
      return
  
imports: import os
  import glob
  import tensorflow
  import numpy
  from shutil import rmtree
  from tqdm import tqdm
  from deepneuro.models.model import DeepNeuroModel
  from deepneuro.utilities.util import add_parameter
label_desc: Use image node_id 4 for calling the TensorFlowModel obj's underlying member method code with example usage: obj.process_step() without return types