human
Give me a the Python code you requested given the following:
2. Run BO using parallel evaluation of batched suggestions
assistant
#Here is the Python code you requested.
from GPy.models import GPRegression
from emukit.model_wrappers import GPyModelWrapper
from emukit.core.initial_designs.latin_design import LatinDesign
from emukit.core import ParameterSpace, ContinuousParameter
from emukit.core.loop import UserFunctionWrapper, UserFunctionResult
from emukit.core.loop.stopping_conditions import FixedIterationsStoppingCondition
from emukit.core.optimization import GradientAcquisitionOptimizer
from emukit.bayesian_optimization.loops import BayesianOptimizationLoop
from emukit.bayesian_optimization.acquisitions import NegativeLowerConfidenceBound

import warnings
warnings.filterwarnings('ignore')  # to quell the numerical errors in hyperparameter fitting

# Plotting stuff (from constrained optimization tutorial)
x1b, x2b = _ps.get_bounds()
plot_granularity = 50
x_1 = np.linspace(x1b[0], x1b[1], plot_granularity)
x_2 = np.linspace(x2b[0], x2b[1], plot_granularity)
x_1_grid, x_2_grid = np.meshgrid(x_1, x_2)
x_all = np.stack([x_1_grid.flatten(), x_2_grid.flatten()], axis=1)
y_all = _branin(x_all)
y_reshape = np.reshape(y_all, x_1_grid.shape)
x_best = np.array([(-pi,12.275), (pi,2.275), (9.425,2.475)])

def plot_progress(loop_state, batch_size: int):
    plt.figure(figsize=FIG_SIZE)
    plt.contourf(x_1, x_2, y_reshape)
    plt.plot(loop_state.X[:-batch_size, 0], loop_state.X[:-batch_size, 1], linestyle='', marker='.', markersize=16, color='b')
    plt.plot(loop_state.X[-batch_size:, 0], loop_state.X[-batch_size:, 1], linestyle='', marker='.', markersize=16, color='r')
    plt.plot(x_best[:,0], x_best[:,1], linestyle='', marker='x', markersize=18, color='g')
    plt.legend(['Previously evaluated points', 'Last evaluation', 'True best'])
    plt.show()

async def async_run_bo():
    # Configure
    max_iter = 50
    n_init = 6
    batch_size = 6
    beta = 0.1  # tradeoff parameter for NCLB acq. opt.
    update_interval = 1  # how many results before running hyperparam. opt.
    # Build Bayesian optimization components
    space = _ps
    design = LatinDesign(space)
    X_init =...