SavedModelBundle memory leak

System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 18.04.5 LTS
• TensorFlow installed from (source or binary): Included in java artifact
• TensorFlow version: 2.4.1

Describe the current behavior
Loading and closing SavedModelBundle in an infinite loop leads to OutOfMemoryError:

Exception in thread "main" java.lang.OutOfMemoryError: Physical memory usage is too high: physicalBytes (7828M) > maxPhysicalBytes (7828M)
	at org.bytedeco.javacpp.Pointer.deallocator(Pointer.java:695)
	at org.tensorflow.internal.c_api.AbstractTF_Status.newStatus(AbstractTF_Status.java:70)
	at org.tensorflow.SavedModelBundle.load(SavedModelBundle.java:401)
	at org.tensorflow.SavedModelBundle.access$000(SavedModelBundle.java:59)
	at org.tensorflow.SavedModelBundle$Loader.load(SavedModelBundle.java:68)
	at org.tensorflow.SavedModelBundle.load(SavedModelBundle.java:242)
	at com.mycompany.app.App.main(App.java:13)

I use 0.3.1 version of the library. It is the only dependency:

<dependency>
  <groupId>org.tensorflow</groupId>
  <artifactId>tensorflow-core-platform</artifactId>
  <version>0.3.1</version>
</dependency>

The memory leak is reproducible with a model from this repository:
https://github.com/tensorflow/java/tree/master/tensorflow-core/tensorflow-core-api/src/test/resources/saved_model

Describe the expected behavior

I assume that the following code should run endlessly

Code to reproduce the issue

package com.mycompany.app;

import org.tensorflow.SavedModelBundle;
import java.net.URISyntaxException;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) throws URISyntaxException {
        var modelPath = Paths.get(App.class.getResource("/saved_model").toURI()).toString();
        while (true) {
            var model = SavedModelBundle.load(modelPath, "serve");
            model.close();
        }
    }
}

Inizia con la riproduzione in App.main, concentrandoti sulle chiamate ripetute a SavedModelBundle.load(modelPath, "serve") e model.close(), e utilizza la risorsa saved_model indicata nell'issue. Verifica che la memoria continui a crescere tra le iterazioni e che il percorso di caricamento e chiusura possa essere completato ripetutamente senza un OutOfMemoryError.