NSFWjs not working in lambda

I tried using nsfwjs to detect images upload to s3 as safe or not but it is not working in lambda, I tried increasing memory to 10GB. But it still timed out :(

Log:

START RequestId: 18f3c1c7-3341-4973-b35c-b638cadd851e Version: $LATEST
2021-06-21T03:34:53.247Z    18f3c1c7-3341-4973-b35c-b638cadd851e    WARN
============================
Hi there :wave:. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================
END RequestId: 18f3c1c7-3341-4973-b35c-b638cadd851e
REPORT RequestId: 18f3c1c7-3341-4973-b35c-b638cadd851e    Duration: 30033.57 ms    Billed Duration: 30000 ms    Memory Size: 10240 MB    Max Memory Used: 121 MB    Init Duration: 606.54 ms
2021-06-21T03:35:23.277Z 18f3c1c7-3341-4973-b35c-b638cadd851e Task timed out after 30.03 seconds

Code (for local testing):

//  const aws = require('aws-sdk');
const tf = require('@tensorflow/tfjs');
const sizeOf = require('image-size');
const jpeg = require('jpeg-js');
const nsfwjs = require('nsfwjs')
const axios = require('axios')


//const s3 = new aws.S3({ apiVersion: '2006-03-01' });



//exports.handler = async (event, context) => {
async function abc(){
  const model = await nsfwjs.load();

  const readImage = buf => {
    var imgprodata = sizeOf(buf)
    if(imgprodata.type != "JPEG" && imgprodata.type != "JPG" && imgprodata.type != "jpeg"&& imgprodata.type != "jpg"){
      let opt = {
        height: imgprodata.height,
        width: imgprodata.width,
        data: buf,
      }
      const pixels = jpeg.encode(opt,100)
      return pixels
    } else {
    const pixels = jpeg.decode(buf, true)
    return pixels
    }
  }
  
  const imageByteArray = (image, numChannels) => {
    const pixels = image.data
    const numPixels = image.width * image.height;
    const values = new Int32Array(numPixels * numChannels);
  
    for (let i = 0; i < numPixels; i++) {
      for (let channel = 0; channel < numChannels; ++channel) {
        values[i * numChannels + channel] = pixels[i * 4 + channel];
      }
    }
  
    return values
  }
  
  const imageToInput = (image, numChannels) => {
    const values = imageByteArray(image, numChannels)
    const outShape = [image.height, image.width, numChannels];
    const input = tf.tensor3d(values, outShape, 'int32');

  
    return input;
  }

  
  // const key = event.Records[0].s3.object.key;
  const key = '7c1e158e-26a8-44a1-b219-bf85741e4754'

  var pic = await axios.get(`https://public-usercontent.dogegram.xyz/storage/${key}`, {
    responseType: 'arraybuffer'
  });

  var pix = await readImage(pic.data);
  var img = await imageToInput(pix, 3)

  
  const predictions = await model.classify(img)



  console.log(predictions)

  return 'done'
  
}
//}


abc()

For testing:

npm init -y
npm i @tensorflow/tfjs image-size jpeg-js nsfwjs axios