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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def run_mnist_DistilledSGLD(num_training=50000, gpu_id=None):
 """Run DistilledSGLD on mnist dataset"""

    X, Y, X_test, Y_test = load_mnist(num_training)
    minibatch_size = 100
    if num_training >= 10000:
        num_hidden = 800
        total_iter_num = 1000000
        teacher_learning_rate = 1E-6
        student_learning_rate = 0.0001
        teacher_prior = 1
        student_prior = 0.1
        perturb_deviation = 0.1
    else:
        num_hidden = 400
        total_iter_num = 20000
        teacher_learning_rate = 4E-5
        student_learning_rate = 0.0001
        teacher_prior = 1
        student_prior = 0.1
        perturb_deviation = 0.001
    teacher_net = get_mnist_sym(num_hidden=num_hidden)
    logsoftmax = LogSoftmax()
    student_net = get_mnist_sym(output_op=logsoftmax, num_hidden=num_hidden)
    data_shape = (minibatch_size,) + X.shape[1::]
    teacher_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
                           'softmax_label': nd.zeros((minibatch_size,), ctx=dev(gpu_id))}
    student_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
                           'softmax_label': nd.zeros((minibatch_size, 10), ctx=dev(gpu_id))}
    teacher_initializer = BiasXavier(factor_type="in", magnitude=1)
    student_initializer = BiasXavier(factor_type="in", magnitude=1)
    student_exe, student_params, _ = \
        DistilledSGLD(teacher_sym=teacher_net, student_sym=student_net,
                      teacher_data_inputs=teacher_data_inputs,
                      student_data_inputs=student_data_inputs,
                      X=X, Y=Y, X_test=X_test, Y_test=Y_test, total_iter_num=total_iter_num,
                      student_initializer=student_initializer,
                      teacher_initializer=teacher_initializer,
                      student_optimizing_algorithm="adam",
                      teacher_learning_rate=teacher_learning_rate,
                      student_learning_rate=student_learning_rate,
                      teacher_prior_precision=teacher_prior, student_prior_precision=student_prior,
                      perturb_deviation=perturb_deviation, minibatch_size=100, dev=dev(gpu_id))