为什么GPU运行时SparseFeat中vocabulary_size的值大小不会引起错误

[T0L0ve]

代码就是demo中的例子，例子中SparseFeat的vocabulary_size大小设置为最大值+1

fixlen_feature_columns = [SparseFeat(feat, data[feat].max() + 1,embedding_dim=4)
                              for feat in sparse_features]

当我将其改为1时代码仍能成功运行

fixlen_feature_columns = [SparseFeat(feat, 1,embedding_dim=4)
                              for feat in sparse_features]

而当我禁用gpu，使用cpu训练模型时tensorflow则会提示我越界错误

[zanshuxun]

请补充运行环境信息 例如python/tf/cuda版本等，便于我们复现问题并进行排查

Describe the bug(问题描述) A clear and concise description of what the bug is.Better with standalone code to reproduce the issue.

To Reproduce(复现步骤) Steps to reproduce the behavior:

Go to '...' Click on '....' Scroll down to '....' See error Operating environment(运行环境):

python version [e.g. 3.6, 3.7] tensorflow version [e.g. 1.4.0, 1.15.0, 2.10.0] deepctr version [e.g. 0.9.2,] Additional context Add any other context about the problem here.