uploadModel = async (tflite, displayName, tags = null) => {
  console.log('Uploading model to Cloud Storage...');
  const files = await storageBucket.upload(tflite);
  const bucket = files[0].metadata.bucket;
  const name = files[0].metadata.name;
  const gcsUri = `gs://${bucket}/${name}`;

  const modelSpec = {
    displayName: displayName,
    tfliteModel: { gcsTfliteUri: gcsUri },
  };
  if (tags != null) { modelSpec.tags = tags; }
  const model = await ml.createModel(modelSpec);

  await ml.publishModel(model.modelId);

  const tagList = model.tags == null ? '' : model.tags.join(", ");
  console.log('Model uploaded and published:');
  console.log(`${model.displayName}\t\t${model.modelId}\t ${tagList}`);
}
Upload a TensorFlow Lite model to your project and publish it.

@param {string} tflite Path to the tflite file you want to upload.
@param {string} displayName A name to identify the model in your Firebase
 project. This is the name you use from your app to load the model.
@param {?Array<string>=} tags Optional tags to help with model management.