class_docstr: Hook that loads a checkpoint after the session is created.
  
  >>> from tensorflow.python.ops import variables as tf_variables
  >>> from tensorflow.python.training.monitored_session import SingularMonitoredSession
  >>>
  >>> tensors = create_graph()
  >>> saver = tf.train.Saver(var_list=tf_variables.trainable_variables())
  >>> checkpoint_hook = LoadCheckpoint(saver, my_checkpoint_file)
  >>> with tf.SingularMonitoredSession(hooks=[checkpoint_hook]) as session:
  >>>      while not session.should_stop():
  >>>           session.run(tensors)
code: def __init__(self, saver, checkpoint):
      self._saver = saver
      self._checkpoint = checkpoint
      self._loaded = False
  
imports: import json
  import os
  import time
  import tensorflow
  from tensorflow.python.training import session_run_hook
  from tensorlayer import logging
  from tensorlayer.decorators import deprecated
  from tensorlayer.lazy_imports import LazyImport
label_desc: Use image node_id 1 to create a new LoadCheckpoint object from inherited base classes: session_run_hook with example: obj = LoadCheckpoint(saver, checkpoint)