I'm trying to set my initial hidden states in the lstm network using a dense layer but, I have been having problems with using multiple inputs in my network. The flow of the network looks something like below. The dense layer between static features (input) and init_hidden_states should be trainable. So far, I tried using a DAG network but, failed due to an error (If there is sequential input layer, there can't be any other input layer present). In my case the sequential input layer is used for time series input and I'm using an image input layer to input the static features. Any advice or example is appreciated. Thank you.