anchor.diff:  
   image::src/test/resources/TensorFlowProcessorArcutectureOverview.png[]
   
  +== Input
  +
  +=== Headers
  +
  +N/A
  +
  +=== Payload
  +
   The TensorFlow Processor uses a `TensorflowInputConverter` to convert the input data into data format compliant with the
   TensorFlow Model used. The input converter converts the input `Messages` into key/value `Map`, where
   the Key corresponds to a model input placeholder and the content is `org.tensorflow.DataType` compliant value.
   The default converter implementation expects either Map payload or flat json message that can be converted into a Map.
   
   The `TensorflowInputConverter` can be extended and customized. See link::../spring-cloud-starter-stream-processor-twitter-sentiment/src/main/java/org/springframework/cloud/stream/app/twitter/sentiment/processor/TwitterSentimentTensorflowInputConverter.java[TwitterSentimentTensorflowInputConverter.java] for example.
  -
  -Processor's output uses `TensorflowOutputConverter` to convert the computed `Tensor` result into a serializable
  -message. The default implementation uses `Tuple` triple.
  -
  -Custom `TensorflowOutputConverter` can provide more convenient data representations.
  -See link::../spring-cloud-starter-stream-processor-twitter-sentiment/src/main/java/org/springframework/cloud/stream/app/twitter/sentiment/processor/TwitterSentimentTensorflowOutputConverter.java[TwitterSentimentTensorflowOutputConverter.java].
  -
   
   Following snippet shows how to export any `TensorFlow` model (trained as well) into `ProtocolBuffer` binary format as required by the Processor.
   ```python
  
positive.code: tf.train.write_graph(minimal_graph, SAVE_DIR, 'my_graph.proto', as_text=False)
  tf.train.write_graph(minimal_graph, SAVE_DIR, 'my.txt', as_text=True)
  ```
  
  == Options
  
  
negative.code: == Build
  
  ```
  $ ./mvnw clean install -PgenerateApps
  $ cd apps
  ```
  You can find the corresponding binder based projects here.
  You can then cd into one of the folders and build it:
  ```
  $ ./mvnw clean package
  ```
  
  == Examples
  
  ```
  java -jar tensorflow-processor.jar --model= --modelFetch= --mode="
  ```
  