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Testing: Trained Bert2Bert model with MLIR bridge enabled and then NHNet based on Bert2Bert checkpoint, also with MLIR bridge enabled. Exported this to a saved model, ran inference, and confirmed model performance, accuracy, and prediction parity with the model trained without the MLIR bridge enabled.
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* Merged commit includes the following changes:
326723075  by Andre Araujo:

    Move image resize utility into utils.py.

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* Adding back matched_images_demo.png

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Update to TF 2 for deep_speech

i.e., the checkpoint will be saved using
tf.train.Checkpoint(FLAGS.checkpoint_model_name=model)

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* Merged commit includes the following changes:
326369548  by Andre Araujo:

    Fix import issues.

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326159826  by Andre Araujo:

    Changed the implementation of the cosine weights from Keras layer to tf.Variable to manually control for L2 normalization.

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326139082  by Andre Araujo:

    Support local feature matching using ratio test.

    To allow for easily choosing which matching type to use, we rename a flag/argument and modify all related files to avoid breakages.

    Also include a small change when computing nearest neighbors for geometric matching, to parallelize computation, which saves a little bit of time during execution (argument "n_jobs=-1").

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326119848  by Andre Araujo:

    Option to measure DELG latency taking binarization into account.

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324316608  by Andre Araujo:

    DELG global features training.

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323693131  by Andre Araujo:

    PY3 conversion for delf public lib.

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321046157  by Andre Araujo:

    Purely Google refactor

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* Added export of delg_model module.
Co-authored-by: Andre Araujo <andrearaujo@google.com>

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Fix QQP preprocessing for test set. Test set does not contain qid1 or qid2 columns, so the current try/except block causes the entire test set to be ignored.

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* Input/Output tweaks for YAMNet and VGGish.

- Waveform input for YAMNet is now padded so that we get at least
  one patch of log mel spectrogram. The VGGish TF-Hub exporter
  uses YAMNet's feature computation so the VGGish export will
  also pad waveform input similarly.
- Added a 1024-D embedding output to YAMNet so we now produce
  predicted scores, log mel spectrogram features, and embeddings,
  to satisfy a variety of uses: class prediction, acoustic
  feature visualization, semantic feature extraction.
- Simplified usage of YAMNet in inference mode. Instead of trying
  to work around implicit batch size issues in the Model.predict()
  API, we simply __call__() the Model.
- Switched inference.py to TF 2 and Eager execution.
- Updated the visualization notebook: now uses TF2/Eager and
  can be loaded and run in Google Colab.

* Responded to DAn's comments in https://github.com/tensorflow/models/pull/9092

- Merged spectrogram compu...

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Project name seems that have changed to project ID. Just changed the project names to project Id.

Add ops for extracting labels from sentences (get_*_labels() functions). This CL includes the ops for extracting labels for:
- BERT's next sentence prediction task
- ALBERT's sentence order prediction task

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