Merged commit includes the following changes:

175703479  by yifeif:

    Internal change.

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175695370  by A. Unique TensorFlower:

    Implements _shared_embedding_columns and adds some tests.

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175695349  by A. Unique TensorFlower:

    Implements tf.metrics.true_negatives, adds missing tests and does some cleanup in tf.contrib.metrics.

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PiperOrigin-RevId: 175703479

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+# TensorFlow Lite Optimizing Converter (TOCO) Python API reference
+
+## High-level overview
+
+While the TensorFlow Lite Optimizing Converter can be used from the command
+line, it is often convenient to use it as part of Python model build and
+training script. This is so that conversion can be part of your model
+development pipeline. This allows you to know early and often that you are
+designing a model that can be targeted to devices with mobile.
+
+## API
+
+In Python you can run `help(tf.contrib.lite)` to get documentation on functions.
+In particular, `tf.contrib.lite.toco_convert` presents a simple API and
+`tf.contrib.lite.toco_from_protos` allows more detailed control of TOCO using
+the protobuf interface to TOCO.
+
+## Example
+
+In particular, here we show creating a simple model and converting it to a
+TensorFlow Lite Model.
+
+```python
+import tensorflow as tf
+
+img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
+val = img + tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.])
+out = tf.identity(val, name="out")
+with tf.Session() as sess:
+  tflite_model = tf.contrib.lite.toco_convert(sess.graph_def, [img], [out])
+  open("test.tflite", "wb").write(tflite_modeL)
+```
+
+**NOTE** Currently, the TOCO command will cause a fatal error to the Python
+interpreter when TOCO conversion fails. This will be remedied as soon as
+possible.
+
+## Example 2: Export with variables
+
+If a model has variables, they need to be turned into constants. This process is
+known as freezing, and it can actually be accomplished with
+
+```python
+import tensorflow as tf
+
+img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
+var = tf.get_variable("weights", dtype=tf.float32, shape=(1,64,64,3))
+val = img + var
+
+def canonical_name(x):
+  return x.name.split(":")[0]
+
+out = tf.identity(val, name="out")
+with tf.Session() as sess:
+  sess.run(tf.global_variables_initializer())
+  out_tensors = [out]
+  frozen_graphdef = tf.graph_util.convert_variables_to_constants(
+      sess, sess.graph_def, map(canonical_name, out_tensors))
+  tflite_model = tf.contrib.lite.toco_convert(
+      frozen_graphdef, [img], out_tensors)
+  open("converted_model.tflite", "wb").write(tflite_model)
+```