how can I use the hmc method to approximate the non gaussian likelihood, thank you

I set the Possion distribution pdf as my GP likelihood, and want to use hmc method to infer its parameter. Here is my code as follows:

poisson_likelihood = GPy.likelihoods.Poisson()
kernel = GPy.kern.RBF(input_dim, variance=1.0, lengthscale=1.0)
hmc_inf= GPy.inference.mcmc.HMC()
m = GPy.core.GP(X=train_X_scaled, Y=train_Y_dl_total_scaled, likelihood=poisson_likelihood, inference_method=hmc_inf, kernel= kernel)

the error shows:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-97-6f996d532af2> in <module>()
      3 poisson_likelihood = GPy.likelihoods.Poisson()
      4 kernel = GPy.kern.RBF(input_dim, variance=1.0, lengthscale=1.0)
----> 5 hmc_inf= GPy.inference.mcmc.HMC()
      6 m = GPy.core.GP(X=train_X_scaled, Y=train_Y_dl_total_scaled, likelihood=poisson_likelihood, inference_method=hmc_inf, kernel= kernel)
      7 

TypeError: __init__() missing 1 required positional argument: 'model'

So I want to know is there anyway that I can use the hmc to infer the parameters of an arbitrary likelihood? Thank you for your help!!!

Start with GPy.inference.mcmc.HMC and inspect how its required model argument is used. Then review the GP construction and Poisson likelihood integration to determine whether HMC supports this case or what usage guidance is missing. Done means documenting the supported workflow and limitations for arbitrary likelihoods.