如何在Tensorflow移动android应用程序上使用预训练模型？

dependencies {
    implementation 'org.tensorflow:tensorflow-lite:2.x.x'
}

import android.os.Bundle;
import androidx.appcompat.app.AppCompatActivity;
import org.tensorflow.lite.Interpreter;
import java.io.FileInputStream;
import java.io.IOException;
import java.nio.MappedByteBuffer;
import java.nio.channels.FileChannel;

public class MainActivity extends AppCompatActivity {

    private Interpreter tflite;

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);

        try {
            // Load the TensorFlow Lite model.
            tflite = new Interpreter(loadModelFile(this, "your_model.tflite"));
        } catch (IOException e) {
            e.printStackTrace();
        }

        // Example: Run inference
        float[][] input = new float[1][224]; // Example input shape
        float[][] output = new float[1][1000]; // Example output shape
        tflite.run(input, output);

        // Process the output as needed
    }

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