What's happening in my LSTM layer?

In building a deep neural network, especially using some of the higher level frameworks such as Keras, we often don't fully understand what's happening in each layer. The sequential model will get you far indeed, but when it's time to do something more complex or intriguing, you will need to dive into the details. In this article, I'm going to explain exactly what's happening as you pass a batch of data through an LSTM layer with an example from PyTorch. I want to note that, I won't be covering any of the exact mechanics of the LSTM cells or why they are useful. If you're reading this, you're probably aware of the vanishing gradient problem and understand the basics of the gating mechanisms.