message: Documentation changes for Converter Overview page.
diff: # TensorFlow Lite Converter
  -The TensorFlow Lite Converter takes a TensorFlow graph file and creates a graph
  -file used by the TensorFlow Lite interpreter.
  +TensorFlow Lite uses the optimized
  +[FlatBuffer](https://google.github.io/flatbuffers/) format to represent graphs.
  +Therefore, a TensorFlow model
  +([protocol buffer](https://developers.google.com/protocol-buffers/)) needs to be
  +converted into a `FlatBuffer` file before deploying to clients.
  ## From model training to device deployment
  -After a TensorFlow model is trained, the TensorFlow Lite converter uses that
  -model to generate a TensorFlow Lite
  -[FlatBuffer](https://google.github.io/flatbuffers/) file (`.tflite`). The
  -converter supports as input:
  -[SavedModels](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators),
  -frozen graphs (models generated by
  -[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py)),
  -and `tf.keras` HDF5 models. The TensorFlow Lite `FlatBuffer` file is deployed to
  -a client device (generally a mobile or embedded device), and the TensorFlow Lite
  -interpreter uses the compressed model for on-device inference. This conversion
  -process is shown in the diagram below:
  +The TensorFlow Lite converter generates a TensorFlow Lite
  +[FlatBuffer](https://google.github.io/flatbuffers/) file (`.tflite`) from a
  +TensorFlow model.
  +
  +The converter supports the following input formats:
  +
  +* [SavedModels](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators)
  +* Frozen `GraphDef`: Models generated by
  + [freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py).
  +* `tf.keras` HDF5 models.
  +* Any model taken from a `tf.Session` (Python API only).
  +
  +The TensorFlow Lite `FlatBuffer` file is then deployed to a client device
  +(generally a mobile or embedded device), and the TensorFlow Lite interpreter
  +uses the compressed model for on-device inference. This conversion process is
  +shown in the diagram below:
  ![TFLite converter workflow](../images/convert/workflow.svg)
  -The TensorFlow Lite Converter can be used either from [Python](python_api.md) or
  -from the [command line](cmdline_examples.md). This allows you to integrate the
  -conversion step into the model design workflow, ensuring the model is easy to
  -convert to a mobile inference graph.
  +## Options
  +
  +The TensorFlow Lite Converter can be used from either of these two options:
  +
  +* [Python](python_api.md) (**Preferred**): Using the Python API makes it
  + easier to convert models as part of a model development pipeline, and helps
  + mitigate [compatibility](../tf_ops_compatibility.md) issues early on.
  +* [Command line](cmdline_examples.md)
  