text: const {Tensor3D, tensor3d, util} = require('@tensorflow/tfjs-core');
  const jpeg = require('jpeg-js');
  
  /**
   * Decode a JPEG-encoded image to a 3D Tensor of dtype `int32`.
   *
   * ```js
   * const image = require('path/to/img.jpg');
   * const imageAssetPath = Image.resolveAssetSource(image);
   * const response = await fetch(imageAssetPath.uri, {}, { isBinary: true });
   * const rawImageData = await response.arrayBuffer();
   * const imageTensor = decodeJpeg(rawImageData);
   * ```
   *
   * @param contents The JPEG-encoded image in an Uint8Array.
   * @param channels An optional int. Defaults to 3. Accepted values are
   *     0: use the number of channels in the JPG-encoded image.
   *     1: output a grayscale image.
   *     3: output an RGB image.
   * @returns A 3D Tensor of dtype `int32` with shape [height, width, 1/3].
   */
  /** @doc {heading: 'Media', subheading: 'Images'} */
  function decodeJpeg(
      contents, channels = 3) {
    util.assert(
        getImageType(contents) === 'jpeg',
        () => 'The passed contents are not a valid JPEG image');
    util.assert(
        channels === 3, () => 'Only 3 channels is supported at this time');
    const TO_UINT8ARRAY = true;
    const {width, height, data} = jpeg.decode(contents, TO_UINT8ARRAY);
    // Drop the alpha channel info because jpeg.decode always returns a typedArray
    // with 255
    const buffer = new Uint8Array(width * height * 3);
    let offset = 0;  // offset into original data
    for (let i = 0; i < buffer.length; i += 3) {
      buffer[i] = data[offset];
      buffer[i + 1] = data[offset + 1];
      buffer[i + 2] = data[offset + 2];
  
      offset += 4;
    }
  
    return tensor3d(buffer, [height, width, channels]);
  }
  
  /**
   * Helper function to get image type based on starting bytes of the image file.
   */
  function getImageType(content) {
    // Classify the contents of a file based on starting bytes (aka magic number:
    // tslint:disable-next-line:max-line-length
    // https://en.wikipedia.org/wiki/Magic_number_(programming)#Magic_numbers_in_files)
    // This aligns with TensorFlow Core code:
    // tslint:disable-next-line:max-line-length
    // https://github.com/tensorflow/tensorflow/blob/4213d5c1bd921f8d5b7b2dc4bbf1eea78d0b5258/tensorflow/core/kernels/decode_image_op.cc#L44
    if (content.length > 3 && content[0] === 255 && content[1] === 216 &&
        content[2] === 255) {
      // JPEG byte chunk starts with `ff d8 ff`
      return 'jpeg';
    } else if (
        content.length > 4 && content[0] === 71 && content[1] === 73 &&
        content[2] === 70 && content[3] === 56) {
      // GIF byte chunk starts with `47 49 46 38`
      return 'gif';
    } else if (
        content.length > 8 && content[0] === 137 && content[1] === 80 &&
        content[2] === 78 && content[3] === 71 && content[4] === 13 &&
        content[5] === 10 && content[6] === 26 && content[7] === 10) {
      // PNG byte chunk starts with `\211 P N G \r \n \032 \n (89 50 4E 47 0D 0A
      // 1A 0A)`
      return 'png';
    } else if (content.length > 3 && content[0] === 66 && content[1] === 77) {
      // BMP byte chunk starts with `42 4d`
      return 'bmp';
    } else {
      throw new Error(
          'Expected image (JPEG, PNG, or GIF), but got unsupported image type');
    }
  }
  
  module.exports = {
    decodeJpeg
  }
  