How to use the pyfrc.physics.motor_cfgs function in pyfrc

def test_tankdrive_get_distance():
    """Test TankModel.get_distance() by driving in a quarter circle.
    If the turn angle is 90deg, then x should equal y. get_distance() runs in little steps,
    so multiple calls should be equivalent to a single call"""

    # just use the default robot
    tank = tankmodel.TankModel.theory(
        motor_cfgs.MOTOR_CFG_CIM,
        robot_mass=90 * units.lbs,
        gearing=10.71,
        nmotors=2,
        x_wheelbase=2.0 * units.feet,
        wheel_diameter=6 * units.inch,
    )
    # slight turn to the right
    l_motor = 1.0
    r_motor = -0.9

    # get the motors up to speed, so that subsequent calls produce the same result
    tank.calculate(l_motor, r_motor, 2.0)

    # figure out how much time is needed to turn 90 degrees
    angle = 0.0
    angle_target = -math.pi / 2.0  # 90 degrees

def __init__(self, physics_controller):
        """
            :param physics_controller: `pyfrc.physics.core.Physics` object
                                       to communicate simulation effects to
        """

        self.physics_controller = physics_controller

        #
        # Two ways of initializing the realistic physics -- either use the
        # ka/kv that you computed for your robot, or use the theoretical model
        #

        # Change these parameters to fit your robot!
        # -> these parameters are for the test robot mentioned in the paper
        motor_cfg = motor_cfgs.MOTOR_CFG_CIM
        robot_mass = 110 * units.lbs

        bumper_width = 3.25 * units.inch
        robot_wheelbase = 22 * units.inch
        robot_width = 23 * units.inch + bumper_width * 2
        robot_length = 32 * units.inch + bumper_width * 2

        wheel_diameter = 3.8 * units.inch
        drivetrain_gear_ratio = 6.1
        motors_per_side = 3

        # Uses theoretical parameters by default, change this if you've
        # actually measured kv/ka for your robot
        if True:

            self.drivetrain = tankmodel.TankModel.theory(

def __init__(self, physics_controller):
        """
            :param physics_controller: `pyfrc.physics.core.Physics` object
                                       to communicate simulation effects to
        """

        self.physics_controller = physics_controller

        #
        # Two ways of initializing the realistic physics -- either use the
        # ka/kv that you computed for your robot, or use the theoretical model
        #

        # Change these parameters to fit your robot!
        # -> these parameters are for the test robot mentioned in the paper
        motor_cfg = motor_cfgs.MOTOR_CFG_CIM
        robot_mass = 110 * units.lbs

        bumper_width = 3.25 * units.inch
        robot_wheelbase = 22 * units.inch
        robot_width = 23 * units.inch + bumper_width * 2
        robot_length = 32 * units.inch + bumper_width * 2

        wheel_diameter = 3.8 * units.inch
        drivetrain_gear_ratio = 6.1
        motors_per_side = 3

        # Uses theoretical parameters by default, change this if you've
        # actually measured kv/ka for your robot
        if True:

            self.drivetrain = tankmodel.TankModel.theory(