是的，变分自编码器仅依据潜在表示生成分布。因此，我们需要使用潜在表示来生成新的样本或对现有样本进行重构。下面给出一个示例代码，该代码显示如何使用潜在表示生成新样本：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# 创建变分自编码器
latent_dim = 2

encoder_inputs = keras.Input(shape=(28, 28, 1))
x = layers.Conv2D(32, 3, activation="relu", strides=2, padding="same")(encoder_inputs)
x = layers.Conv2D(64, 3, activation="relu", strides=2, padding="same")(x)
x = layers.Flatten()(x)
x = layers.Dense(16, activation="relu")(x)

z_mean = layers.Dense(latent_dim, name="z_mean")(x)
z_log_var = layers.Dense(latent_dim, name="z_log_var")(x)

def sampling(args):
    z_mean, z_log_var = args
    epsilon = tf.keras.backend.random_normal(shape=(tf.keras.backend.shape(z_mean)[0], latent_dim),
                                      dtype=tf.float32)
    return z_mean + tf.keras.backend.exp(0.5 * z_log_var) * epsilon

z = layers.Lambda(sampling, output_shape=(latent_dim,), name="z")([z_mean, z_log_var])

encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name="encoder")

latent_inputs = keras.Input(shape=(latent_dim,))
x = layers.Dense(7 * 7 * 64, activation="relu")(latent_inputs)
x = layers.Reshape((7, 7, 64))(x)
x = layers.Conv2DTranspose(64, 3, activation="relu", strides=2, padding="same")(x)
x = layers.Conv2DTranspose(32, 3, activation="relu", strides=2, padding="same")(x)
decoder_outputs = layers.Conv2DTranspose(1, 3, activation="sigmoid", padding="same")(x)

decoder = keras.Model