move EI docs from TFS Rest API docs to TFS python docs

src/sagemaker/tensorflow/deploying_python.rst

@@ -25,6 +25,15 @@ like this:
 
 The code block above deploys a SageMaker Endpoint with one instance of the type 'ml.c4.xlarge'.
 
+TensorFlow serving on SageMaker has support for `Elastic Inference <https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html>`_, which allows for inference acceleration to a hosted endpoint for a fraction of the cost of using a full GPU instance. In order to attach an Elastic Inference accelerator to your endpoint provide the accelerator type to ``accelerator_type`` to your ``deploy`` call.
+
+.. code:: python
+
+  predictor = estimator.deploy(initial_instance_count=1,
+                               instance_type='ml.c5.xlarge',
+                               accelerator_type='ml.eia1.medium'
+                               endpoint_type='tensorflow-serving-elastic-inference')
+
 What happens when deploy is called
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

src/sagemaker/tensorflow/deploying_tensorflow_serving.rst

@@ -34,15 +34,6 @@ estimator object to create a SageMaker Endpoint:
 
 The code block above deploys a SageMaker Endpoint with one instance of the type 'ml.c5.xlarge'.
 
-TensorFlow serving on SageMaker has support for `Elastic Inference <https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html>`_, which allows for inference acceleration to a hosted endpoint for a fraction of the cost of using a full GPU instance. In order to attach an Elastic Inference accelerator to your endpoint provide the accelerator type to ``accelerator_type`` to your ``deploy`` call.
-
-.. code:: python
-
-  predictor = estimator.deploy(initial_instance_count=1,
-                               instance_type='ml.c5.xlarge',
-                               accelerator_type='ml.eia1.medium'
-                               endpoint_type='tensorflow-serving-elastic-inference')
-
 What happens when deploy is called
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^