import torch

class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
    def forward(self, x):
        return 16 / x.shape[0]

model = Model()
inputs = (
    torch.randn(2, 16, 11),
)

eager_outputs = model(*inputs)

ep = torch.jit.trace(model, inputs)

import coremltools as ct
import numpy as np

mlmodel = ct.convert(ep, inputs = [ct.TensorType(shape=[2,16,11])], convert_to='mlprogram')