How to use arrays in PyGPGO?

Question

I'm looking at Bayesian optimization through the package, PyGPGO, which uses PyMC3 under the hood, fitting a Gaussian process to the underlying function and proposing movements in order to maximize the reward.

So far, it seems really easy to define a function in terms of scalar values, especially when parameters are limited. However, I'm not sure if there is a way to supply vector/array arguments.

from pyGPGO.covfunc import squaredExponential
from pyGPGO.acquisition import Acquisition
from pyGPGO.surrogates.GaussianProcess import GaussianProcess
from pyGPGO.GPGO import GPGO

def f(x):
    return (np.sin(x))


sexp = squaredExponential()
gp = GaussianProcess(sexp)
acq = Acquisition(mode='ExpectedImprovement')
param = {'x': ('cont', [0, 2 * np.pi])}

np.random.seed(23)
gpgo = GPGO(gp, acq, f, param)
gpgo.run(max_iter=20)

What I would like is something along the lines of:

#        var:  type          , range
param = {'x': (np.array(3,10), [0, 2 * np.pi])}

If this isn't possible, I'll likely set up multiple variables in the form of {x1: ..., x2: ...,} and then access them accordingly via the function definition, itself, though this seems suboptimal when the parameter space is large.

Edit: Here's my approach, which is not working...

import numpy as np
from pyGPGO.covfunc import squaredExponential
from pyGPGO.acquisition import Acquisition
from pyGPGO.surrogates.GaussianProcess import GaussianProcess
from pyGPGO.GPGO import GPGO

param = {f'x_{i}': ('cont', [0, 1]) for i in range(3)}
param.update({f'y_{i}': ('cont', [0, 1]) for i in range(3)})

def f(p):
    v1 = np.array([p[f'x_{i}'] for i in range(3)])
    v2 = np.array([p[f'y_{i}'] for i in range(3)])
    return np.dot(v1,v2)


sexp = squaredExponential()
gp = GaussianProcess(sexp)
acq = Acquisition(mode='ExpectedImprovement')

np.random.seed(23)
gpgo = GPGO(gp, acq, f, param)
gpgo.run(max_iter=20)

And the error:

Evaluation   Proposed point       Current eval.      Best eval.
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-49-f58f602dca8c> in <module>()
     26 np.random.seed(23)
     27 gpgo = GPGO(gp, acq, f, param)
---> 28 gpgo.run(max_iter=20)

1 frames
/usr/local/lib/python3.7/dist-packages/pyGPGO/GPGO.py in _firstRun(self, n_eval)
     86             s_param_val = list(s_param.values())
     87             self.X[i] = s_param_val
---> 88             self.y[i] = self.f(**s_param)
     89         self.GP.fit(self.X, self.y)
     90         self.tau = np.max(self.y)

TypeError: f() got an unexpected keyword argument 'x_1'

So it seems that wrapping parameters up into a dictionary is a bit trickier than I expected, as the parameters in the object param must be explicitly called by the function, f, not received as a dictionary, p.

Has anyone solved this problem before?