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

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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,]
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