[{'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 minimise_system_energy(L, m_init):\nprint("L={:9}, {} ".format(L, m_init.__name__), end="")\nN = 16  # discretisation in one dimension\ncubesize = 100e-9  # cube edge length (m)\ncellsize = cubesize/N  # discretisation in all three dimensions.\nlex = cubesize/L  # exchange length.\nKm = 1e6  # magnetostatic energy density (J/m**3)\nMs = np.sqrt(2*Km/oc.mu0)  # magnetisation saturation (A/m)\nA = 0.5 * oc.mu0 * Ms**2 * lex**2  # exchange energy constant\nK = 0.1*Km  # Uniaxial anisotropy constant\nu = (0, 0, 1)  # Uniaxial anisotropy easy-axis\np1 = (0, 0, 0)  # Minimum sample coordinate.\np2 = (cubesize, cubesize, cubesize)  # Maximum sample coordinate.\ncell = (cellsize, cellsize, cellsize)  # Discretisation.\nmesh = oc.Mesh(p1=(0, 0, 0), p2=(cubesize, cubesize, cubesize),\ncell=(cellsize, cellsize, cellsize))  # Create a mesh object.\nsystem = oc.System(name="stdprob3")\nsystem.hamiltonian = oc.Exchange(A) + oc.UniaxialAnisotropy(K, u) + oc.Demag()\nsystem.m = df.Field(mesh, value=m_init, norm=Ms)\nmd = oc.MinDriver()\nmd.drive(system)\nreturn system'}, {'role': 'assistant', 'content': 'modelling'}]