# How exactly keras LSTM layer works?

I try to create a sentiment analysis that have 7 classification. Let's say, I have 100.000 unique word (already converted into 100.000 integer) which have the longest input is 41. I created 3 layer embedding, LSTM and Dense Layer.

model = Sequential();
model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])


Based on code above, I tried to create the illustration model as below :

From my understanding, Xt is 41 integer that will be an input. The embedding layer will convert every integer into real-valued vector of length 50. Every vector that has been converted will be an input of LSTM layer (X1 - X41)

1. How many LSTM units created based on the code (as my understanding is 41) ?
Because of return_sequences & return_state by default false for LSTM layer, so it will become many to one ?
If so, h41 is the only output value that will be used for classification ?