How to use the dill.dump_session function in dill

def dump_session(file_path):
  """Pickle the current python session to be used in the worker.

  Note: Due to the inconsistency in the first dump of dill dump_session we
  create and load the dump twice to have consistent results in the worker and
  the running session. Check: https://github.com/uqfoundation/dill/issues/195
  """
  dill.dump_session(file_path)
  dill.load_session(file_path)
  return dill.dump_session(file_path)

def dump_session(file_path):
  """Pickle the current python session to be used in the worker.

  Note: Due to the inconsistency in the first dump of dill dump_session we
  create and load the dump twice to have consistent results in the worker and
  the running session. Check: https://github.com/uqfoundation/dill/issues/195
  """
  dill.dump_session(file_path)
  dill.load_session(file_path)
  return dill.dump_session(file_path)

def train(self, batch_size, inputs, labels, num_epochs, learning_rate, filename):
        self.batch_size = batch_size
        self.learning_rate = learning_rate
        self.check_training_data(self.batch_size, inputs, labels)
        for j in range(num_epochs):
            i = 0
            print("== EPOCH: ", j, " ==")
            while i+batch_size != len(inputs):
                self.error = 0
                self.forward_pass(inputs[i:i+batch_size])
                self.calculate_error(labels[i:i+batch_size])
                self.back_pass(labels[i:i+batch_size])
                i += batch_size
            self.error /= batch_size
            print("Error: ", self.error)
        dill.dump_session(filename)

i = 0
            print("== EPOCH: ", j, " ==")
            while i+batch_size != len(inputs):
                self.error = 0  
                # input_batch = []
                # label_batch = []
                # # print(i)
                # for i in range(i, i+batch_size):
                #     input_batch.append(inputs[i])
                #     label_batch.append(labels[i])   
                self.forward_pass(inputs[i:i+batch_size])
                self.calculate_error(labels[i:i+batch_size])
                self.back_pass(labels[i:i+batch_size])
                i += batch_size
            print("Error: ", self.error)
        dill.dump_session(filename)

def persist_env(file):
    """
    Persist an entire environment on file. This function depends on Dill package

    :param file: file name to store the environment
    """
    dill.dump_session(file)

i = 0
            print("== EPOCH: ", j, " ==")
            while i+batch_size != len(inputs):
                self.error = 0  
                input_batch = []
                label_batch = []
                # print(i)
                for i in range(i, i+batch_size):
                    input_batch.append(inputs[i])
                    label_batch.append(labels[i])   
                self.forward_pass(input_batch)
                self.calculate_error(label_batch)
                self.back_pass(label_batch)
                i += 1
            print("Error: ", self.error)
        dill.dump_session(filename)

def dump_session(file_path):
  """For internal use only; no backwards-compatibility guarantees.

  Pickle the current python session to be used in the worker.

  Note: Due to the inconsistency in the first dump of dill dump_session we
  create and load the dump twice to have consistent results in the worker and
  the running session. Check: https://github.com/uqfoundation/dill/issues/195
  """
  with _pickle_lock_unless_py2:
    dill.dump_session(file_path)
    dill.load_session(file_path)
    return dill.dump_session(file_path)

def dump_session(file_path):
  """For internal use only; no backwards-compatibility guarantees.

  Pickle the current python session to be used in the worker.

  Note: Due to the inconsistency in the first dump of dill dump_session we
  create and load the dump twice to have consistent results in the worker and
  the running session. Check: https://github.com/uqfoundation/dill/issues/195
  """
  with _pickle_lock_unless_py2:
    dill.dump_session(file_path)
    dill.load_session(file_path)
    return dill.dump_session(file_path)

def train(self, batch_size, inputs, labels, num_epochs, learning_rate, filename):
        self.batch_size = batch_size
        self.learning_rate = learning_rate
        for j in range(num_epochs):
            i = 0
            print("== EPOCH: ", j, " ==")
            while i + batch_size != len(inputs):
                self.error = 0
                self.forward_pass(inputs[i : i + batch_size])
                self.calculate_error(labels[i : i + batch_size])
                self.back_pass(labels[i : i + batch_size])
                i += batch_size
            print("Error: ", self.error)
        dill.dump_session(filename)