Generate the implementation of SGD

def SGD(sym, data_inputs, X, Y, X_test, Y_test, total_iter_num,
        lr=None,
        lr_scheduler=None, prior_precision=1,
        out_grad_f=None,
        initializer=None,
        minibatch_size=100, dev=mx.gpu()):
    """Generate the implementation of SGD"""
    if out_grad_f is None:
        label_key = list(set(data_inputs.keys()) - set(['data']))[0]
    exe, params, params_grad, _ = get_executor(sym, dev, data_inputs, initializer)
    optimizer = mx.optimizer.create('sgd', learning_rate=lr,
                                    rescale_grad=X.shape[0] / minibatch_size,
                                    lr_scheduler=lr_scheduler,
                                    wd=prior_precision)
    updater = mx.optimizer.get_updater(optimizer)
    start = time.time()
    for i in range(total_iter_num):
        indices = numpy.random.randint(X.shape[0], size=minibatch_size)
        X_batch = X[indices]
        Y_batch = Y[indices]
        exe.arg_dict['data'][:] = X_batch
        if out_grad_f is None:
            exe.arg_dict[label_key][:] = Y_batch
            exe.forward(is_train=True)
            exe.backward()
        else:
            exe.forward(is_train=True)
            exe.backward(out_grad_f(exe.outputs, nd.array(Y_batch, ctx=dev)))
        for k in params:
            updater(k, params_grad[k], params[k])
        if (i + 1) % 500 == 0:
            end = time.time()
            print("Current Iter Num: %d" % (i + 1), "Time Spent: %f" % (end - start))
            sample_test_acc(exe, X=X_test, Y=Y_test, label_num=10, minibatch_size=100)
            start = time.time()
    return exe, params, params_grad