FFI is faster the method channels, and I couldn't find a good example for this so I've made one.

This example app is working as it is on Android and iOS. No further dependency needs to be installed. Tested on Flutter 3.3.8

1. Create FFI plugin from this guide
2. Android: copied native_pytorch/libtorch-android and setup native_pytorch/android/CMakeLists.txt (If anyone wondering, I got the .so and header files from the original .aar pytorch package, just unzipep and taken from the jni/ and headers/ )
3. iOS: setup native_pytorch/ios/native_pytorch.podspec (note that s.static_framework = true is mandatory)
4. iOS: drag native_pytorch/ios/Classes/native_pytorch.cpp to xCode to projects files (on the left side under the runner). Otherwise it say symbols not found

The inference is significantly faster through FFI on Android. Moreover, with bigger input tensors, I was getting an OutOfMemoryError before, and now with the FFI it's totally fine.

The current version of the package:
Input shape (1, 3, 350, 350) - inference 886ms
Input shape (1, 3, 700, 700) -  inference 3050ms
Input shape (1, 3, 2000, 2000) -  inference `OutOfMemoryError`
Input shape (1, 3, 8750, 8750) -  inference `OutOfMemoryError`

FFI:
Input shape (1, 3, 350, 350) - inference 250ms
Input shape (1, 3, 700, 700) -  inference 314ms
Input shape (1, 3, 2000, 2000) -  inference 580ms
Input shape (1, 3, 8750, 8750) -  inference 12114ms

(I measured the time to make the model inference from Dart List, and get the output to Dart List, so the conversions to/from C++ data structures are already included in the measures. Testing ML model was LDC)