message: lite/micro: Add feature buffer to micro_speech example.
  This fixes
  Accumulate feature slices in separate buffer.
  The input tensor is not suitable for keeping state across interference
  as it has limited lifetime and the buffer space may be reused.
diff: @@ -43,6 +43,8 @@ int32_t previous_time = 0;
  // determined by experimentation.
  constexpr int kTensorArenaSize = 10 * 1024;
  uint8_t tensor_arena[kTensorArenaSize];
  +uint8_t feature_buffer[kFeatureElementCount];
  +uint8_t* model_input_buffer = nullptr;
  } // namespace
  // The name of this function is important for Arduino compatibility.
  @@ -104,12 +106,13 @@ void setup() {
  error_reporter->Report("Bad input tensor parameters in model");
  return;
  }
  + model_input_buffer = model_input->data.uint8;
  // Prepare to access the audio spectrograms from a microphone or other source
  // that will provide the inputs to the neural network.
  // NOLINTNEXTLINE(runtime-global-variables)
  static FeatureProvider static_feature_provider(kFeatureElementCount,
  - model_input->data.uint8);
  + feature_buffer);
  feature_provider = &static_feature_provider;
  static RecognizeCommands static_recognizer(error_reporter);
  @@ -136,6 +139,11 @@ void loop() {
  return;
  }
  + // Copy feature buffer to input tensor
  + for(int i = 0; i < kFeatureElementCount; i++) {
  + model_input_buffer[i] = feature_buffer[i];
  + }
  +
  // Run the model on the spectrogram input and make sure it succeeds.
  TfLiteStatus invoke_status = interpreter->Invoke();
  if (invoke_status != kTfLiteOk) {
  