Using RNN and LSTM for process control using available data mesaurements

I have 50 sample measurements of input (temperature) with 600 time steps in each sample. I also have the outputs for these 50 samples in form of 4 variables (like saturation, concentration, lower bound concentration and upper bound concentration).

So the input data is 50 samples, 600 timesteps each and 1 variable or property. The output is 50 samples, 600 timesteps each and 4 variables or properties (and the variable 2 concentration must always be between the variable 3 and 4 which are its lower and upper bounds at all times)

I am new to deep learning and LSTMs. I am having trouble implementing the LSTM. So from what I have read (kindly correct me if I am wrong), I need to use many to many LSTM and batch_size = 50 input_shape is (600,) seq_len = 1

How many units of LSTM to use? I am very confused how to implement LSTM in this.