How to run inference with dynamic shape model ?

Hi bro,
I have a model that supports dynamic shape, but tflite does not support it when converting, but it does support it from the code.

Doc from TFL
https://www.tensorflow.org/lite/guide/inference#run_inference_with_dynamic_shape_model

If you want to run a model with dynamic input shape, resize the input shape before running inference. Otherwise, the None shape in Tensorflow models will be replaced by a placeholder of 1 in TFLite models.

The following examples show how to resize the input shape before running inference in different languages. All the examples assume that the input shape is defined as [1/None, 10], and need to be resized to [3, 10].

// Resize input tensors before allocate tensors
interpreter->ResizeInputTensor(/*tensor_index=*/0, std::vector<int>{3,10});
interpreter->AllocateTensors();

How to operate with react-native-fast-tflite

Start with the linked TensorFlow Lite dynamic-shape inference documentation and the react-native-fast-tflite usage entry points. Determine whether the exposed API supports resizing the input before tensor allocation, and document the supported steps or limitations with a working example; the answer should be verifiable by running dynamic-shape inference.