Add documentation on loading TF extension libraries for running certa…

…in TF models in DJL (#1776)

docs/tensorflow/how_to_import_tensorflow_models_in_DJL.md

@@ -52,6 +52,44 @@ Please refer to these two examples:
 1. [Object Detection with TensorFlow](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java) for loading from TensorFlow Hub url.
 2. [BERT Classification](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/BertClassification.java) for loading from local downloaded model.
 
+## How to import TensorFlow models that use [TensorFlow Extensions](https://www.tensorflow.org/resources/libraries-extensions)
+
+You can use DJL to run TensorFlow models that require ops available in TensorFlow extensions (not in core), such as models that use ops in  [TensorFlow Text](https://www.tensorflow.org/text).
+
+To use such models in DJL you must know:
+* Which extension contains the additional required ops
+* The TensorFlow extension library version that is compatible with the version of Tensorflow currently being used in DJL
+
+Here we show an example that uses DJL to run the [Multilingual Universal Sentence Encoder](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3). 
+This is a text encoder from tensorflow hub that uses ops from the TensorFlow Text extension.
+* You need to download the library files for the extension so that we can load them for use. [Here](https://github.com/tensorflow/text#install-using-pip) are instructions for downloading TensorFlow text. We'll use version `2.7.0` in this example.
+* After the library files are downloaded, you are ready to load them for use in DJL. You do this by using
+TensorFlow Java `loadLibrary(...)` API before loading the model in DJL. 
+* We can update the existing [UniversalSentenceEncoder](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/UniversalSentenceEncoder.java) example to use the Multilingual model:
+
+```java
+// Build Criteria for Multilingual Universal Sentence Encoder from TensorFlow Hub
+String modelUrl = "https://storage.googleapis.com/tfhub-modules/google/universal-sentence-encoder-multilingual/3.tar.gz"
+Criteria<String[], float[][]> criteria =
+    Criteria.builder()
+        .optApplication(Application.NLP.TEXT_EMBEDDING)
+        .setTypes(String[].class, float[][].class)
+        .optModelUrls(modelUrl)
+        .optTranslator(new MyTranslator())
+        .optEngine("TensorFlow")
+        .optProgress(new ProgressBar())
+        .build();
+
+// Load TensorFlow Text libraries for necessary ops
+// Example Path of installation on Linux may look like: /home/<user>/.local/python-3.8.3/lib/python3.8/site-packages/tensorflow_text/
+TensorFlow.loadLibrary("<path_to_library_installation>/python/ops/_sentencepiece_tokenizer.so");
+
+// Run the model
+try (ZooModel<String[], float[][]> model = criteria.loadModel();
+    Predictor<String[], float[][]> predictor = model.newPredictor()) {
+    return predictor.predict(inputs.toArray(new String[0]));
+}
+```
 ## How to load TensorFlow Checkpoints
 
 To load an TensorFlow Estimator checkpoint, you need to convert it to SavedModel format in using Python.