code: def train_all_epochs(self, start_epoch=1):
      n_epochs = self.run_config.n_epochs
      learning_rate = self.run_config.init_lr
      batch_size = self.run_config.batch_size
  
      total_start_time = time.time()
      for epoch in range(start_epoch, n_epochs + 1):
          print(
              "\n", "-" * 30, "Train epoch: %d" % epoch, "-" * 30, "\n"
          )
          start_time = time.time()
          new_lr = self.run_config.learning_rate(epoch)
          if new_lr != learning_rate:
              learning_rate = new_lr
              print(
                  "Decrease learning rate, new lr = %f" % learning_rate
              )
  
          print("Training...")
          loss, acc = self.train_one_epoch(
              self.data_provider.train, batch_size, learning_rate
          )
          # save logs about "loss" and "acc" if the option is true
          if self.run_config.should_save_logs:
              self.log_loss_accuracy(loss, acc, epoch, prefix="train")
  
          if (
              self.run_config.validation_frequency
              and epoch % self.run_config.validation_frequency == 0
          ):
              print("Validation...")
              loss, acc = self.test(
                  self.data_provider.validation, batch_size
              )
              if self.run_config.should_save_logs:
                  self.log_loss_accuracy(
                      loss, acc, epoch, prefix="valid"
                  )
              if self.run_config.should_save_model:
                  self.save_model()
                  json.dump(
                      {"epoch": epoch + 1},
                      open(
                          "%s/checkpoint/epoch.info" % self._path, "w"
                      ),
                  )
  
          time_per_epoch = time.time() - start_time
          seconds_left = int((n_epochs - epoch) * time_per_epoch)
          print(
              "Time per epoch: %s, Est. complete in: %s"
              % (
                  str(timedelta(seconds=time_per_epoch)),
                  str(timedelta(seconds=seconds_left)),
              )
          )
  
      if self.run_config.should_save_model:
          self.save_model()
  
      total_training_time = time.time() - total_start_time
      print(
          "\nTotal training time: %s"
          % str(timedelta(seconds=total_training_time))
      )
  
imports: import os
  import shutil
  import tensorflow
  import numpy
  import time
  from datetime import timedelta
  import json
  import pickle
label_desc: Use image node_id 7 for calling the BasicModel obj's underlying member method code with example usage: obj.train_all_epochs(start_epoch) without return types