Understanding of number of cells in layers of sequential models I am trying to teach myself RNN, but I have a question.
And so, imagine 2 layers: an input layer with three neurons $(x1, x2, x3)$ and a classic recurrent layer with 2 neurons and an activation function f. I will write out the outputs of each neuron of the recurrent layer. $ht1 = f (W * [x1, [0, 0, N]] + b) ht2 = f (W * [x2, ht1] + b)$. It turns out that $x3$ is not used, what to do in this case?
And also, let's imagine a slightly different RNN architecture.
An input layer with two neurons $(x1, x2)$ and a classic recurrent layer with 3 neurons and an activation function f. I will write out the outputs of each neuron of the recurrent layer. $ht1 = f (W * [x1, [0, 0, N] + bias]) ht2 = f (W * [x2, ht1] + bias)$. It turns out that the 3rd neuron of the RNN layer is not used, what to do in this case?
Please help me figure out how the neural network works in these cases. Thanks!
UPD:
I realized that i don't know how recurrent neural  networks work if  number of neurons in recurrent layer doesn't equal(!=) number of inputs
I have one thought:number of inputs has to always be equal number of neurons in RNN layer.
But code below contradicts with my guess.
model = Sequential()
model.add(Embedding(maxWordsCount, 256, input_length = inp_words))
model.add(SimpleRNN(128, activation='tanh'))
model.add(Dense(maxWordsCount, activation='softmax'))
model.summary()

That's model for predicting next word.
RNN cell versus neuron
You are hesitating between RNN cells and neurons. I understand you refer to RNN cells in your question. And so, traditionally any layer of a sequence model will always have the same number of cells as the length of your sequence or embedding size.
See difference between cell and neuron here: Difference between cell state and hidden state
See input size for sequence models here: How can I picture an unfolded RNN as a normal Feed Forward Network?
Re; code snippet
In SimpleRNN, the first argument is the number of units in each cell i.e. number of neurons in each cell. The number of cells is not an argument in sequential layers, and always is equal to the length of your input sequences or size of embedding layer.
model = Sequential()
model.add(Embedding(maxWordsCount, 256, input_length = inp_words))
# Below, 128 is the neurons of each cell, and relates to the cell memory capacity.
model.add(SimpleRNN(128, activation='tanh'))