问由于Adafruit_Tensorflow_Lite库中缺少文件，无法在adafruit电路操场蓝鳍金枪鱼上编译tensorflow lite示例

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#include <TensorFlowLite.h>
#include "Adafruit_TFLite.h"
#include "Adafruit_Arcada.h"

#include "output_handler.h"
#include "sine_model_data.h"

// Create an area of memory to use for input, output, and intermediate arrays.
// Finding the minimum value for your model may require some trial and error.
const int kTensorAreaSize  (2 * 1024);

// This constant represents the range of x values our model was trained on,
// which is from 0 to (2 * Pi). We approximate Pi to avoid requiring additional
// libraries.
const float kXrange = 2.f * 3.14159265359f;

// Will need tuning for your chipset
const int kInferencesPerCycle = 200;
int inference_count = 0;

Adafruit_Arcada arcada;
Adafruit_TFLite ada_tflite(kTensorAreaSize);

// The name of this function is important for Arduino compatibility.
void setup() {
  Serial.begin(115200);
  //while (!Serial) yield();

  arcada.arcadaBegin();
  // If we are using TinyUSB we will have the filesystem show up!
  arcada.filesysBeginMSD();
  arcada.filesysListFiles();
  // Set the display to be on!
  arcada.displayBegin();
  arcada.setBacklight(255);
  arcada.display->fillScreen(ARCADA_BLUE);
  
  if (! ada_tflite.begin()) {
    arcada.haltBox("Failed to initialize TFLite");
    while (1) yield();
  }
  if (arcada.exists("model.tflite")) {
    arcada.infoBox("Loading model.tflite from disk!");
    if (! ada_tflite.loadModel(arcada.open("model.tflite"))) {
      arcada.haltBox("Failed to load model file");
    }
  } else if (! ada_tflite.loadModel(g_sine_model_data)) {
    arcada.haltBox("Failed to load default model");
  }
  Serial.println("\nOK");

  // Keep track of how many inferences we have performed.
  inference_count = 0;
}

// The name of this function is important for Arduino compatibility.
void loop() {
  // Calculate an x value to feed into the model. We compare the current
  // inference_count to the number of inferences per cycle to determine
  // our position within the range of possible x values the model was
  // trained on, and use this to calculate a value.
  float position = static_cast<float>(inference_count) /
                   static_cast<float>(kInferencesPerCycle);
  float x_val = position * kXrange;

  // Place our calculated x value in the model's input tensor
  ada_tflite.input->data.f[0] = x_val;

  // Run inference, and report any error
  TfLiteStatus invoke_status = ada_tflite.interpreter->Invoke();
  if (invoke_status != kTfLiteOk) {
    ada_tflite.error_reporter->Report("Invoke failed on x_val: %f\n",
                           static_cast<double>(x_val));
    return;
  }

  // Read the predicted y value from the model's output tensor
  float y_val = ada_tflite.output->data.f[0];

  // Output the results. A custom HandleOutput function can be implemented
  // for each supported hardware target.
  HandleOutput(ada_tflite.error_reporter, x_val, y_val);

  // Increment the inference_counter, and reset it if we have reached
  // the total number per cycle
  inference_count += 1;
  if (inference_count >= kInferencesPerCycle) inference_count = 0;
}