def apply_embeddings(self, inputs):
    'T5的embedding只有token embedding，\n        并把relative position embedding准备好，待attention使用。\n        '
    x = inputs
    x = self.apply(inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token')
    x = self.apply(inputs=x, layer=Dropout, rate=self.dropout_rate, name='Encoder-Embedding-Dropout')
    if (self.embedding_size != self.hidden_size):
        x = self.apply(inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Encoder-Embedding-Mapping')
    return x
T5的embedding只有token embedding，
并把relative position embedding准备好，待attention使用。
def apply_embeddings(self, inputs):
    'T5的embedding只有token embedding，\n        并把relative position embedding准备好，待attention使用。\n        '
    x = inputs
    x = self.apply(inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token')
    x = self.apply(inputs=x, layer=Dropout, rate=self.dropout_rate, name='Encoder-Embedding-Dropout')
    if (self.embedding_size != self.hidden_size):
        x = self.apply(inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Encoder-Embedding-Mapping')
    return x