Keras: why must an embedding layer be used only as the first layer?

"can only be used as the first layer in a model." This makes no sense to me

Generally, an embedding layer maps discrete values to continues values. In the subsequence layers, we have continues vector representation that means there is no need to convert the vectors again.

I might want to do a reshape/flatten on input before passing it to the embedding layer

Of course, you can reshape or flatten an input but in most cases is meaningless. For example, assume we have sentences with a length of 30 and want to flatten them before passed them to embedding:

input_layer = Input(shape=(30))
flatten = Flatten()(input_layer)
embedd = Embedding(1000, 100)(flatten)

In the above example, flatten layer has no effect at all. Before and after flatten our vector size is [batch, 30].

Let look at another example, assume our inputs vector our 2D with the shape of [batch, 30, 2]. After flatting the input, the vectors have the size of [batch, 60]. We can feed them into Embedding layer but in most of the scenarios, it has no meaning. In fact, we destroy the logical relationship between features.

input_layer = Input(shape=(30, 2))
flatten = Flatten()(input_layer)
embedd = Embedding(1000, 100)(flatten)