cannot deploy YAMNet model to SageMaker

Question

I followed this tutorial and had the model fine-tuned.

the model-saving part of serving model is like this:

saved_model_path = 'dogs_and_cats_yamnet/yamnet-model/00000001'

input_segment = tf.keras.layers.Input(shape=(), dtype=tf.float32, name='audio')
embedding_extraction_layer = hub.KerasLayer(yamnet_model_handle,
                                            trainable=False, name='yamnet')
_, embeddings_output, _ = embedding_extraction_layer(input_segment)
serving_outputs = my_model(embeddings_output)
serving_outputs = ReduceMeanLayer(axis=0, name='classifier')(serving_outputs)
serving_model = tf.keras.Model(input_segment, serving_outputs)
serving_model.save(saved_model_path, include_optimizer=False)

Then followed this page, uploading the model to S3 and deploying the model.

!tar -C "$PWD" -czf dogs_and_cats_yamnet.tar.gz dogs_and_cats_yamnet/
model_data = Session().upload_data(path="dogs_and_cats_yamnet.tar.gz", key_prefix="model")
model = TensorFlowModel(model_data=model_data, role=sagemaker_role, framework_version="2.3")
predictor = model.deploy(initial_instance_count=1, instance_type="ml.c5.xlarge")

Deployment seems successful, but when I try to do inference,

waveform = np.zeros((3*48000), dtype=np.float32)
result = predictor.predict(waveform)

the following error occurs.

ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (400) from primary with message "{
    "error": "The first dimension of paddings must be the rank of inputs[1,2] [1,144000]\n\t [[{{node yamnet_frames/tf_op_layer_Pad/Pad}}]]"

I have no idea why this happens. I am struggling with it for hours and coming up with no clue. YAMNet works fine when I pulled the model from tf hub directly and take inference with it. This is kind of a minor question I guess, but I would appreciate any helpful answers. Thank you in advance.