如何获取tf.data.Dataset的特征和张量的字典？

import tensorflow as tf

# 创建一个tf.data.Dataset对象
dataset = tf.data.Dataset.from_tensor_slices((features, labels))

# 定义特征和张量的字典
feature_columns = {
    'feature1': tf.feature_column.numeric_column('feature1'),
    'feature2': tf.feature_column.categorical_column_with_vocabulary_list('feature2', vocabulary_list),
}
tensor_dict = {
    'tensor1': tensor1,
    'tensor2': tensor2,
}

# 转换每个样本为特征和张量的字典
def map_fn(feature, label):
    return {
        'feature1': feature['feature1'],
        'feature2': feature['feature2'],
        'tensor1': tensor_dict['tensor1'],
        'tensor2': tensor_dict['tensor2'],
    }, label

dataset = dataset.map(map_fn)

# 获取特征和张量的字典的结构
element_spec = dataset.element_spec
feature_spec = element_spec[0]
tensor_spec = element_spec[1]

# 打印特征和张量的名称、形状、数据类型等信息
for feature_name, feature_spec in feature_spec.items():
    print('Feature:', feature_name)
    print('Shape:', feature_spec.shape)
    print('Dtype:', feature_spec.dtype)

for tensor_name, tensor_spec in tensor_spec.items():
    print('Tensor:', tensor_name)
    print('Shape:', tensor_spec.shape)
    print('Dtype:', tensor_spec.dtype)