code: def train(
      self,
      training_data_collection,
      validation_data_collection=None,
      **kwargs,
  ):
      self.init_training(training_data_collection, kwargs)
  
      self.init = tf.global_variables_initializer()
      self.sess.run(self.init)
  
      self.callback_process("on_train_begin")
  
      try:
          for epoch in range(self.num_epochs):
              print(("Epoch {}/{}".format(epoch, self.num_epochs)))
              self.callback_process("on_epoch_begin", epoch)
  
              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:
                  self.callback_process("on_batch_begin", step)
  
                  self.process_step(step_counter)
  
                  self.callback_process("on_batch_end", step)
  
              self.callback_process("on_epoch_end", epoch)
  
          self.callback_process("on_train_end")
  
      except KeyboardInterrupt:
          self.callback_process("on_train_end")
      except:
          raise
  
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 3 for calling the TensorFlowModel obj's underlying member method code with example usage: obj.train(training_data_collection, validation_data_collection) without return types