Contiguous array warning in Python (numba)

Question

I have the following snippet of code where I've used numba in order to speed up my code:

import numpy as np
from numba import jit

Sigma = np.array([
                  [1, 1, 0.5, 0.25],
                  [1, 2.5, 1, 0.5],
                  [0.5, 1, 0.5, 0.25],
                  [0.25, 0.5, 0.25, 0.25]
])
Z = np.array([0.111, 0.00658])

@jit(nopython=True)
def mean(Sigma, Z):
  return np.dot(np.dot(Sigma[0:2, 2:4], linalg.inv(Sigma[2:4, 2:4])), Z)

print(mean(Sigma, Z))

However, numba is complaining

NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(float64, 2d, A), array(float64, 2d, F))
  return np.dot(np.dot(Sigma[0:2, 2:4], linalg.inv(Sigma[2:4, 2:4])), Z)

If I'm not mistaken (after reading this), the contiguous structure of the numpy arrays is broken due to the slicing of sub-matrices from Sigma (i.e., "Sigma[0:2, 2:4]"). Is this correct? If so is there any way to resolve this warning? I believe resolving this warning would help speed up my code which is my main goal. Thanks.