Training crashes with DebuggerHookConfig

I have a simple Tensorflow model which I'm training on SageMaker.
It was working fine. Recently it has started crashing while training **right after the first checkpoint is saved**.

Following is the stack trace:

`AssertionError: training/Ftrl/Ftrl/AssignAddVariableOp is not in graph`
```
Traceback (most recent call last):
File "trainer_click_label.py", line 259, in
estimator = train_and_evaluate(train_files, test_files, args.model_dir, args.epochs)
File "trainer_click_label.py", line 133, in train_and_evaluate
tf.estimator.train_and_evaluate(linear_est, train_spec=train_spec, eval_spec=eval_spec)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/training.py", line 473, in train_and_evaluate
return executor.run()
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/training.py", line 613, in run
return self.run_local()
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/training.py", line 714, in run_local
saving_listeners=saving_listeners)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 374, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1164, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1198, in _train_model_default
saving_listeners)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1497, in _train_with_estimator_spec
_, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py", line 784, in run
run_metadata=run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py", line 1289, in run
run_metadata=run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py", line 1390, in run
raise six.reraise(*original_exc_info)
File "/usr/local/lib/python3.6/dist-packages/six.py", line 703, in reraise
raise value
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py", line 1375, in run
return self._sess.run(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py", line 1439, in run
feed_dict, options)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py", line 1466, in _call_hook_before_run
request = hook.before_run(run_context)
File "/usr/local/lib/python3.6/dist-packages/smdebug/tensorflow/session.py", line 362, in before_run
run_context.original_args.fetches,
File "/usr/local/lib/python3.6/dist-packages/smdebug/tensorflow/session.py", line 343, in _filter_to_be_saved
tensor_ref.tf_obj, fetches_ops_tuple, unfilled_placeholders
File "/usr/local/lib/python3.6/dist-packages/smdebug/tensorflow/session.py", line 320, in _is_tensor_dependent_on_unfilled_placeholder
subgraph_nodes = self._get_subgraph_which_reach_fetches(fetches_ops_tuple)
File "/usr/local/lib/python3.6/dist-packages/smdebug/tensorflow/session.py", line 304, in _get_subgraph_which_reach_fetches
subgraph = tf.graph_util.extract_sub_graph(self.graph.as_graph_def(), dest_names)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/util/deprecation.py", line 324, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/graph_util_impl.py", line 197, in extract_sub_graph
_assert_nodes_are_present(name_to_node, dest_nodes)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/graph_util_impl.py", line 152, in _assert_nodes_are_present
assert d in name_to_node, "%s is not in graph" % d
```

Here is a snippet from my code:
```python
hook_config = DebuggerHookConfig(
s3_output_path='s3://ratul-playground/sagemaker/debug_hook/search_linear',
collection_configs=[],
)
estimator = TensorFlow(
source_dir='code',
entry_point=TRAINER_FILE,
model_dir=model_dir,
code_location=OUTPUT_CODE_S3_DIR,
output_path=OUTPUT_TRAINING_S3_DIR,
train_instance_type=train_instance_type,
train_instance_count=1,
hyperparameters=hyperparameters,
sagemaker_session=sess,
role=sagemaker.get_execution_role(),
base_job_name='ranking-linear',
framework_version='2.1',
py_version='py3',
script_mode=True,
debugger_hook_config=hook_config,
tensorboard_output_config=tb_config
)
```

within trainer file:
```python
initial_learning_rate = 0.2
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate, decay_steps=10000, decay_rate=0.96, staircase=True
)

optimizer = tf.keras.optimizers.Ftrl(
learning_rate=lr_schedule, l2_regularization_strength=0.001
)
linear_est = tf.estimator.LinearClassifier(
feature_columns=feature_columns,
n_classes=2,
optimizer=optimizer,
model_dir=os.path.join(model_dir, "checkpoints"),
config=tf.estimator.RunConfig(save_checkpoints_secs=100),
)

train_input_fn = make_input_fn(train_files, num_epochs=num_epochs)
eval_input_fn = make_input_fn(test_files, num_epochs=1, shuffle=False)

# hook = smd.SessionHook.create_from_json_file()
train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=20000)
eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, steps=1000, throttle_secs=0)

tf.estimator.train_and_evaluate(linear_est, train_spec=train_spec, eval_spec=eval_spec)
```

Right now the only way around is disabling the debug-hook by passing `debugger_hook_config=False`.

Please advice.