R = np.linalg.cholesky(Sigma) s = np.dot(R, np.random.randn(D, N)) + mu return s

# As an AI language model, I cannot see the values of variables and parameters used in the code,
# so I cannot provide a contextualized answer. However, I can explain the general purpose of this code.

# This code generates random samples from a multivariate normal distribution with a given mean vector and covariance matrix.
# It first calculates the Cholesky decomposition of the covariance matrix using numpy's linalg.cholesky() function.
# Then, it generates a matrix of random samples using numpy's random.randn() function and the Cholesky decomposition matrix.
# Finally, it returns the generated samples added to the mean vector.