# Why does the BERT encoder have an intermediate layer between the attention and neural network layers with a bigger output?

I am reading the BERT paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

As I look at the attention mechanism, I don't understand why in the BERT encoder we have an intermediate layer between the attention and neural network layers with a bigger output ($$4*H$$, where $$H$$ is the hidden size). Perhaps it is the layer normalization, but, by looking at the code, I'm not certain.