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java.lang.NoSuchMethodError: No virtual method getAccelerationConfig()

I am working on one android application using tenserflow. when I run the application and debug the code it shows java.lang.NoSuchMethodError: No virtual method My code:-

ClassifierActivity.java

public class ClassifierActivity extends CameraActivity implements OnImageAvailableListener {
   private static final boolean MAINTAIN_ASPECT = false;
  private static final Size DESIRED_PREVIEW_SIZE = new Size(960, 480);
  private static final float TEXT_SIZE_DIP = 10;
  private Bitmap rgbFrameBitmap = null;
  private Bitmap croppedBitmap = null;
  private Bitmap cropCopyBitmap = null;
  private long lastProcessingTimeMs;
  private Integer sensorOrientation;
  private Classifier classifier;
  private Matrix frameToCropTransform;
  private Matrix cropToFrameTransform;
  private BorderedText borderedText;

  @Override
  protected int getLayoutId() {
    return R.layout.camera_connection_fragment;
  }

  @Override
  protected Size getDesiredPreviewFrameSize() {
    return DESIRED_PREVIEW_SIZE;
  }

  @Override
  public void onPreviewSizeChosen(final Size size, final int rotation) {
    final float textSizePx =
        TypedValue.applyDimension(
            TypedValue.COMPLEX_UNIT_DIP, TEXT_SIZE_DIP, getResources().getDisplayMetrics());
    borderedText = new BorderedText(textSizePx);
    borderedText.setTypeface(Typeface.MONOSPACE);

    recreateClassifier(getModel(), getDevice(), getNumThreads());
    if (classifier == null) {
      //LOGGER.e("No classifier on preview!");
      return;
    }

    previewWidth = size.getWidth();
    previewHeight = size.getHeight();

    sensorOrientation = rotation - getScreenOrientation();
    //LOGGER.i("Camera orientation relative to screen canvas: %d", sensorOrientation);

    //LOGGER.i("Initializing at size %dx%d", previewWidth, previewHeight);
    rgbFrameBitmap = Bitmap.createBitmap(previewWidth, previewHeight, Config.ARGB_8888);
    croppedBitmap =
        Bitmap.createBitmap(
            classifier.getImageSizeX(), classifier.getImageSizeY(), Config.ARGB_8888);

    frameToCropTransform =
        ImageUtils.getTransformationMatrix(
            previewWidth,
            previewHeight,
            classifier.getImageSizeX(),
            classifier.getImageSizeY(),
            sensorOrientation,
            MAINTAIN_ASPECT);

    cropToFrameTransform = new Matrix();
    frameToCropTransform.invert(cropToFrameTransform);
  }

  @Override
  protected void processImage() {
    rgbFrameBitmap.setPixels(getRgbBytes(), 0, previewWidth, 0, 0, previewWidth, previewHeight);
    final Canvas canvas = new Canvas(croppedBitmap);
    canvas.drawBitmap(rgbFrameBitmap, frameToCropTransform, null);

    runInBackground(
        new Runnable() {
          @Override
          public void run() {
            if (classifier != null) {
              final long startTime = SystemClock.uptimeMillis();
              final List<Classifier.Recognition> results = classifier.recognizeImage(croppedBitmap);
              lastProcessingTimeMs = SystemClock.uptimeMillis() - startTime;
              //LOGGER.v("Detect: %s", results);
              cropCopyBitmap = Bitmap.createBitmap(croppedBitmap);

              runOnUiThread(
                  new Runnable() {
                    @Override
                    public void run() {
                      showResultsInBottomSheet(results);
                      showFrameInfo(previewWidth + "x" + previewHeight);
                      showCropInfo(cropCopyBitmap.getWidth() + "x" + cropCopyBitmap.getHeight());
                      showCameraResolution(canvas.getWidth() + "x" + canvas.getHeight());
                      showRotationInfo(String.valueOf(sensorOrientation));
                      showInference(lastProcessingTimeMs + "ms");
                    }
                  });
            }
            readyForNextImage();
          }
        });
  }

  @Override
  protected void onInferenceConfigurationChanged() {
    if (croppedBitmap == null) {
      // Defer creation until we're getting camera frames.
      return;
    }
    final Device device = getDevice();
    final Model model = getModel();
    final int numThreads = getNumThreads();
    runInBackground(() -> recreateClassifier(model, device, numThreads));
  }

  private void recreateClassifier(Model model, Device device, int numThreads) {
    if (classifier != null) {
      //LOGGER.d("Closing classifier.");
      classifier.close();
      classifier = null;
    }
    if (device == Device.GPU && model == Model.QUANTIZED) {
      //LOGGER.d("Not creating classifier: GPU doesn't support quantized models.");
      runOnUiThread(
          () -> {
            Toast.makeText(this, "GPU does not yet supported quantized models.", Toast.LENGTH_LONG)
                .show();
          });
      return;
    }
    try {
     numThreads);
      classifier = Classifier.create(this, model, device, numThreads);
    } catch (IOException e) {
      //LOGGER.e(e, "Failed to create classifier.");
    }
  }
}

Classifier.java

public abstract class Classifier {
  public enum Model {
    FLOAT,
    QUANTIZED,
  }

  public enum Device {
    CPU,
    NNAPI,
    GPU
  }

  private final int[] intValues = new int[getImageSizeX() * getImageSizeY()];
  private final Interpreter.Options tfliteOptions = new Interpreter.Options();
  private MappedByteBuffer tfliteModel;
  private List<String> labels;
  private GpuDelegate gpuDelegate = null;
  protected Interpreter tflite;
  protected ByteBuffer imgData = null;
  public static Classifier create(Activity activity, Model model, Device device, int numThreads)
      throws IOException {
    if (model == Model.QUANTIZED) {
      return new ClassifierQuantizedMobileNet(activity, device, numThreads);
    } else {
      return new ClassifierFloatMobileNet(activity, device, numThreads);
    }
  }

    private final String id;
    private RectF location;

    public Recognition(
        final String id, final String title, final Float confidence, final RectF location) {
      this.id = id;
      this.title = title;
      this.confidence = confidence;
      this.location = location;
    }

    public String getId() {
      return id;
    }

    public String getTitle() {
      return title;
    }

    public Float getConfidence() {
      return confidence;
    }

    public RectF getLocation() {
      return new RectF(location);
    }

    public void setLocation(RectF location) {
      this.location = location;
    }

    @Override
    public String toString() {
      String resultString = "";
      if (id != null) {
        resultString += "[" + id + "] ";
      }

      if (title != null) {
        resultString += title + " ";
      }

      if (confidence != null) {
        resultString += String.format("(%.1f%%) ", confidence * 100.0f);
      }

      if (location != null) {
        resultString += location + " ";
      }

      return resultString.trim();
    }
  }


  protected Classifier(Activity activity, Device device, int numThreads) throws IOException {
    tfliteModel = loadModelFile(activity);
    switch (device) {
      case NNAPI:
        tfliteOptions.setUseNNAPI(true);
        break;
      case GPU:
        gpuDelegate = new GpuDelegate();
        tfliteOptions.addDelegate(gpuDelegate);
        break;
      case CPU:
        break;
    }
    tfliteOptions.setNumThreads(numThreads);
    tflite = new Interpreter(tfliteModel, tfliteOptions);
    labels = loadLabelList(activity);
    imgData =
        ByteBuffer.allocateDirect(
            DIM_BATCH_SIZE
                * getImageSizeX()
                * getImageSizeY()
                * DIM_PIXEL_SIZE
                * getNumBytesPerChannel());
    imgData.order(ByteOrder.nativeOrder());
    //LOGGER.d("Created a Tensorflow Lite Image Classifier.");
  }

 
  private List<String> loadLabelList(Activity activity) throws IOException {
    List<String> labels = new ArrayList<String>();
    BufferedReader reader =
        new BufferedReader(new InputStreamReader(activity.getAssets().open(getLabelPath())));
    String line;
    while ((line = reader.readLine()) != null) {
      labels.add(line);
    }
    reader.close();
    return labels;
  } 

  private MappedByteBuffer loadModelFile(Activity activity) throws IOException {
    AssetFileDescriptor fileDescriptor = activity.getAssets().openFd(getModelPath());
    FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor());
    FileChannel fileChannel = inputStream.getChannel();
    long startOffset = fileDescriptor.getStartOffset();
    long declaredLength = fileDescriptor.getDeclaredLength();
    return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);
  }

 
  private void convertBitmapToByteBuffer(Bitmap bitmap) {
    if (imgData == null) {
      return;
    }
    imgData.rewind();
    bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
    // Convert the image to floating point.
    int pixel = 0;
    long startTime = SystemClock.uptimeMillis();
    for (int i = 0; i < getImageSizeX(); ++i) {
      for (int j = 0; j < getImageSizeY(); ++j) {
        final int val = intValues[pixel++];
        addPixelValue(val);
      }
    }
    long endTime = SystemClock.uptimeMillis();
    //LOGGER.v("Timecost to put values into ByteBuffer: " + (endTime - startTime));
  }

}

java.lang.NoSuchMethodError: No virtual method getAccelerationConfig()
     Lorg/tensorflow/lite/acceleration/ValidatedAccelerationConfig; 
     in class Lorg/tensorflow/lite/InterpreterImpl$Options; 
     or its super classes (declaration of 
     'org.tensorflow.lite.InterpreterImpl$Options' appears in 
     /data/app/org.tensorflow.lite.examples.detection- 
     w3joIsrUlwpT9hRj_txyLQ==/base.apk!classes12.dex)
        at org.tensorflow.lite.NativeInterpreterWrapper.init(NativeInterpreterWrapper.java:80)
        at org.tensorflow.lite.NativeInterpreterWrapper.<init>(NativeInterpreterWrapper.java:73)
        at org.tensorflow.lite.NativeInterpreterWrapperExperimental.<init>(NativeInterpreterWrapperExperimental.java:36)
        at org.tensorflow.lite.Interpreter.<init>(Interpreter.java:224)
        at org.tensorflow.lite.examples.detection.Currency.tflite.Classifier.<init>(Classifier.java:175)
        at org.tensorflow.lite.examples.detection.Currency.tflite.ClassifierFloatMobileNet.<init>(ClassifierFloatMobileNet.java:41)
        at org.tensorflow.lite.examples.detection.Currency.tflite.Classifier.create(Classifier.java:86)
        at org.tensorflow.lite.examples.detection.Currency.ClassifierActivity.recreateClassifier(ClassifierActivity.java:166)
        at org.tensorflow.lite.examples.detection.Currency.ClassifierActivity.onPreviewSizeChosen(ClassifierActivity.java:70)
        at org.tensorflow.lite.examples.detection.Currency.CameraActivity.onPreviewFrame(CameraActivity.java:235)
        at android.hardware.Camera$EventHandler.handleMessage(Camera.java:1400)
        at android.os.Handler.dispatchMessage(Handler.java:106)

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Answer

Add this in your Manifest file where you have declared activity and exported. android:hardwareAccelerated="true"

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