Format error Exception raised from _load_for_mobile while loading a pytorch model in react native

While trying to make an object detection app in react native using torchvison models encountered the following error –
error –

Possible Unhandled Promise Rejection (id: 1):
Object {
  "message": "Format error
Exception raised from _load_for_mobile at /data/users/atalman/pytorch/torch/csrc/jit/mobile/import.cpp:623 (most recent call first):
(no backtrace available)",
}

reinstalling pytorch did not solve the problem.
followed documentation – > https://playtorch.dev/docs/tutorials/snacks/object-detection/
for version 0.2.4

code App.js –

import {StyleSheet, Text, View} from 'react-native';
import React from 'react';
import {
  Camera,
  MobileModel,
  torch,
  torchvision,
  media,
} from 'react-native-pytorch-core';
let model = null;
const T = torchvision.transforms;
const App = () => {
  async function handleImage(image) {
    console.log('Image Taken!!');
    const width = image.getWidth();
    const height = image.getHeight();
    // 3.ii. Convert image to blob, which is a byte representation of the image
    // in the format height (H), width (W), and channels (C), or HWC for short
    const blob = media.toBlob(image);
    // 3.iii. Get a tensor from image the blob and also define in what format
    // the image blob is.
    let tensor = torch.fromBlob(blob, [height, width, 3]);
    // 3.iv. Rearrange the tensor shape to be [CHW]
    tensor = tensor.permute([2, 0, 1]);
    // 3.v. Divide the tensor values by 255 to get values between [0, 1]
    tensor = tensor.div(255);
    // 3.vi. Crop the image in the center to be a squared image
    const centerCrop = T.centerCrop(Math.min(width, height));
    tensor = centerCrop(tensor);
    // 3.vii. Resize the image tensor to 3 x 224 x 224
    const resize = T.resize(224);
    tensor = resize(tensor);
    // 3.viii. Normalize the tensor image with mean and standard deviation
    const normalize = T.normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]);
    tensor = normalize(tensor);
    // 3.ix. Unsqueeze adds 1 leading dimension to the tensor
    tensor = tensor.unsqueeze(0);
    // console.log(tensor);
    // 3.x. Return the tensor shape [1, 3, 224, 224]
    const result = tensor.shape;
    console.log('result:', result);
    if (model == null) {
      console.log('Loading model...');
      const filePath = await MobileModel.download('detr_resnet50.ptl');
      model = await torch.jit._loadForMobile(filePath);
      console.log('Model successfully loaded');
    }
    console.log('Forward propogation !!');
    const output = await model.forward(tensor);
    console.log(output);
  }
  return (
    <View style={styles.container}>
      <Text style={styles.label}>Class: </Text>
      <Camera style={styles.camera} onCapture={handleImage} />
    </View>
  );
};

export default App;

const styles = StyleSheet.create({
  container: {
    flexGrow: 1,
    backgroundColor: '#ffff',
    padding: 20,
    alignItems: 'center',
  },
  label: {
    marginBottom: 10,
    color: 'black',
    fontSize: 20,
  },
  camera: {
    flexGrow: 1,
    width: '100%',
    marginTop: 70,
  },
});

project dependencies –

  "devDependencies": {
    "@babel/core": "^7.12.9",
    "@babel/runtime": "^7.12.5",
    "@react-native-community/eslint-config": "^2.0.0",
    "babel-jest": "^26.6.3",
    "eslint": "^7.32.0",
    "jest": "^26.6.3",
    "metro-react-native-babel-preset": "0.72.3",
    "react-test-renderer": "18.1.0"
  }