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const mobilenet = await tf.loadLayersModel(
      'https://storage.googleapis.com/tfjs-models/tfjs/mobilenet_v1_0.25_224/model.json');

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//The input size is [null, 224, 224, 3]
const input_s = mobilenet.inputs[0].shape;

//The output size is [null, 1000]
const output_s = mobilenet.outputs[0].shape;

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var pred = mobilenet.predict(tf.zeros([1, 224, 224, 3]));
pred.argMax().print();

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//The number of layers in the model '88'
const len = mobilenet.layers.length;

//this outputs the name of the 3rd layer 'conv1_relu'
const name3 = mobilenet.layers[3].name;

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const layer = mobilenet.getLayer('conv_pw_13_relu');

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mobilenet = tf.model({inputs: mobilenet.inputs, outputs: layer.output});

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 trainableModel = tf.sequential({
    layers: [
      tf.layers.flatten({inputShape: [7, 7, 256]}),
      tf.layers.dense({
        units: 100,
        activation: 'relu',
        kernelInitializer: 'varianceScaling',
        useBias: true
      }),
      tf.layers.dense({

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const activation = mobilenet.predict(input);
const predictions = trainableModel.predict(activation);

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//this outputs a layer of size [null, 7, 7, 256]
const layerOutput = layer.output.shape;

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const tensor = tf.scalar(2);