[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:def hmc_vector(U, gradU, m, dt, nstep, x, X, y, Sigma_beta):\nmean = np.zeros_like(x)\np = np.random.multivariate_normal(mean, m)\noldX = x.copy()\noldEnergy = 0.5 * p.dot(np.linalg.solve(m, p))  + U(x, X, y, Sigma_beta)\nfor i in range(nstep):\np -= gradU(x, X, y, Sigma_beta) * dt/2.\nx += np.linalg.solve(m, p) * dt\np -= gradU(x, X, y, Sigma_beta) * dt/2.\nnewEnergy = 0.5 * p.dot(np.linalg.solve(m, p))  + U(x, X, y, Sigma_beta)\nif np.random.random() > np.exp(oldEnergy - newEnergy): # Metropolis-Hastings\nx = oldX.copy()\nreturn x.copy()'}, {'role': 'assistant', 'content': 'modelling'}]