chore: import upstream snapshot with attribution

tensorflow/lite/g3doc/r1/convert/index.md

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+# TensorFlow Lite converter
+
+The TensorFlow Lite converter takes a TensorFlow model and generates a
+TensorFlow Lite model, which is an optimized
+[FlatBuffer](https://google.github.io/flatbuffers/) (identified by the `.tflite`
+file extension).
+
+Note: This page contains documentation on the converter API for TensorFlow 1.x.
+The API for TensorFlow 2.0 is available
+[here](https://www.tensorflow.org/lite/models/convert).
+
+## Options
+
+The TensorFlow Lite Converter can be used in two ways:
+
+*   [Python API](python_api.md) (**recommended**): Using the Python API makes it
+    easier to convert models as part of a model development pipeline and helps
+    mitigate compatibility issues early on.
+*   [Command line](cmdline_examples.md)
+
+## Workflow
+
+### Why use the 'FlatBuffer' format?
+
+FlatBuffer is an efficient open-source cross-platform serialization library. It
+is similar to [protocol buffers](https://developers.google.com/protocol-buffers)
+used in the TensorFlow model format, with the distinction that FlatBuffers do
+not need a parsing/unpacking step to a secondary representation before data can
+be accessed, avoiding per-object memory allocation. The code footprint of
+FlatBuffers is an order of magnitude smaller than protocol buffers.
+
+### Convert the model
+
+The converter supports the following input formats:
+
+*   [SavedModels](https://www.tensorflow.org/guide/saved_model)
+*   `tf.keras` H5 models.
+*   Frozen `GraphDef` models generated using
+    [freeze_graph.py](https://www.tensorflow.org/code/tensorflow/python/tools/freeze_graph.py).
+*   `tf.Session` models (Python API only).
+
+### Run inference
+
+The TensorFlow Lite model is then deployed to a client 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)