dump

//const embeddings = await getEmbeddings(inputs,use_model);
	//console.log("embeddings made with shape");
	
	//console.log(embeddings.print(true));
	//const inputsDataset = tf.data.array(embeddings);
	//console.log(inputsDataset);
	// const Dataset = tf.data.zip(inputsDataset,labelsDataset);
	// await Dataset.take(3).forEachAsync(e=>console.log(e));


    // return await model.embed(inputs).then(embeddings => {
	// 	// console.log(inputs);
	// 	return new Promise((resolve,reject) => {
	// 		resolve(embeddings.array());
	// 	});
	// });

    // await flatdataset.take(3).forEachAsync(e=>console.log(e));
	
	//const inputs = flatdataset.map(values => { return values.xs;});
	//const labels = flatdataset.map(values => { return values.ys;});
	//console.log(inputs);
	//const labelsDataset = tf.data.array(labels);

	//const embedDataset = await flatdataset.mapAsync(value => await Promise.resolve({xs:getEmbeddings(value.xs,model),ys:value.ys}));

	// // Playground

// use.load().then(model => {
// 	// Embed an array of sentences.
// 	const sentences = [
// 	  'Hello.',
// 	  'How are you?'
// 	];

// 	let zxc = await model.embed(sentences);

// 	zxc.print(true);

// 	model.embed(sentences).then(embeddings => {
// 	  // `embeddings` is a 2D tensor consisting of the 512-dimensional embeddings for each sentence.
// 	  // So in this example `embeddings` has the shape [2, 512].
// 	  embeddings.print(true /* verbose */);
// 	});
//   });

// //END of playground

// return new Promise((resolve,reject) =>{
	// 	resolve({xs:embeds_array,ys:data.ys});
	// });