TFJS error with MobileNetV2

TensorFlow
,
1

I am trying to use Tensorflow JS with the Cifar 10 dataset. But I am constantly getting the following error.

tfjs:17 Uncaught (in promise) Error: Argument ‘images’ passed to ‘resizeBilinear’ must be a Tensor or TensorLike, but got ‘e’

Here is my code

async function getModel() {
    const NUM_OUTPUT_CLASSES = 10;

    // Load the MobileNetV2 model from TensorFlow Hub
    const mobilenetModel = await tf.loadGraphModel(
        "https://tfhub.dev/google/tfjs-model/imagenet/mobilenet_v2_035_224/classification/3/default/1",
        { fromTFHub: true }
    );

    console.log(mobilenetModel); // Check the loaded model
    console.log('Inputs:', mobilenetModel.inputs); // Log the model inputs
    console.log('Outputs:', mobilenetModel.outputs); // Log the model outputs

    // Assuming inputs and outputs are valid, proceed with the model construction
    if (!mobilenetModel.inputs || mobilenetModel.inputs.length === 0) {
        throw new Error('Model inputs are undefined or empty.');
    }

    const input = mobilenetModel.inputs[0]; // Get the first input tensor
    const mobilenetOutput = mobilenetModel.outputs[0]; // Get the output tensor from MobileNetV2

    // Flatten the output from MobileNetV2
    const flatten = tf.layers.flatten().apply(mobilenetOutput);

    // Add a dense layer for further processing
    const dense1 = tf.layers.dense({
        units: 128,
        activation: 'relu',
        kernelInitializer: 'varianceScaling'
    }).apply(flatten);

    // Add the final dense layer with softmax activation for classification
    const output = tf.layers.dense({
        units: NUM_OUTPUT_CLASSES,
        activation: 'softmax'
    }).apply(dense1);

    // Create the final model
    const finalModel = tf.model({
        inputs: input,
        outputs: output
    });

    // Compile the model
    finalModel.compile({
        optimizer: tf.train.adam(),
        loss: 'categoricalCrossentropy',
        metrics: ['accuracy'],
    });

    return finalModel;
}