以下是一个使用BigDL库从自动编码器模型中获取隐藏层输出的示例代码：

from bigdl.nn.layer import *
from bigdl.nn.criterion import *
from bigdl.nn.initialization_method import *
from bigdl.nn.optimizer import *
from bigdl.util.common import *
from bigdl.util.common import Sample
from pyspark import SparkContext
from numpy import array

# 创建SparkContext
sc = SparkContext(appName="BigDLExample")

# 设置环境变量
init_engine()

# 定义自动编码器模型
class Autoencoder(Model):
    def __init__(self, input_size, hidden_size):
        super(Autoencoder, self).__init__()
        self.encoder = Linear(input_size, hidden_size)
        self.decoder = Linear(hidden_size, input_size)

    def forward(self, input):
        encoded = self.encoder.forward(input)
        decoded = self.decoder.forward(encoded)
        return decoded

# 创建自动编码器模型
input_size = 10
hidden_size = 5
autoencoder = Autoencoder(input_size, hidden_size)

# 创建随机输入数据
data = array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
              [11, 12, 13, 14, 15, 16, 17, 18, 19, 20]])

# 将输入数据转换为Sample对象
samples = []
for i in range(data.shape[0]):
    input_data = data[i]
    output_data = data[i]
    sample = Sample.from_ndarray(input_data, output_data)
    samples.append(sample)

# 创建RDD
rdd = sc.parallelize(samples)

# 使用BigDL进行训练
optimizer = Optimizer(
    model=autoencoder,
    training_rdd=rdd,
    criterion=MSECriterion(),
    optim_method=Adam(),
    end_trigger=MaxEpoch(10),
    batch_size=2)
trained_model = optimizer.optimize()

# 获取隐藏层输出
hidden_output = trained_model.encoder.forward(samples[0].features)
print(hidden_output)

请注意，此示例假设您已经正确安装了BigDL库并设置了正确的环境变量。还需要根据您的实际情况调整模型和数据的维度。