It seems you are using some op contained in a layer that is not supported in TensorFlow.js. I do not use Python so maybe someone who is a TF Python expert can tell you what layer is using that SlicingOpLambda and you can remove it and just use regular layers that are more common that are known to convert. After all we have many pose estimation models in TFJS that do work (MoveNet, PoseNet etc) so it seems this just needs to be changed.
Looking at your layers in the example code though you appear to have:
# Define the model
inputs = tf.keras.Input(shape=(51))
embedding = landmarks_to_embedding(inputs)

layer = keras.layers.Dense(128, activation=tf.nn.relu6)(embedding)
layer = keras.layers.Dropout(0.5)(layer)
layer = keras.layers.Dense(64, activation=tf.nn.relu6)(layer)
layer = keras.layers.Dropout(0.5)(layer)
outputs = keras.layers.Dense(len(class_names), activation="softmax")(layer)

model = keras.Model(inputs, outputs)
model.summary()

Tensorflow.js definitely supports layers for dense and dropout 1 so maybe the problem is coming from the embedding line? But that is just a guess as I am not familiar with Python code.
The other option is to write your pre/post processing in JS and then just use the TFJS api instead directly to make your model rather than trying to convert Python ones.
Also check this related post that shows the op support TFJS has:

  
    
    
    TensorFlow.js Op support matrix - if you are having Python model conversion issues, check this first! General Discussion
  
  
    So I have seen a few forum posts around conversion of TF Python models to TensorFlow.js format and sometimes running into an issue where the Python model uses some unsupported op. 
Well, I am pleased to share a nice easy to view spreadsheet that shows the current state of op support in TensorFlow.js across the many backends we support such as CPU, WASM, WebGL, Node, WebGPU (each is like its own version of TensorFlow) for which each op needs to be written in that backend for it to be supported on…