Transformer Encoder Layer

Let’s say we’re designing a deep learning pipeline to process millions of customer support chat logs, and we want to use a Transformer encoder layer built in PyTorch to extract useful representations for downstream tasks like sentiment classification.

How would you approach building this Transformer encoder layer from scratch—what are the essential components, how would you implement them in PyTorch, and why are elements like residual connections and layer normalization critical for stable and effective training?