To implement an Encoder-Decoder architecture with an attention mechanism, use an LSTM-based encoder to process input sequences and an attention-enhanced decoder
Here is the code snippet you can refer to:

In the above code we are using the following key points:
- Uses LSTM Encoder to process sequential input data.
- Integrates an Attention Mechanism to focus on relevant encoder outputs.
- Uses LSTM Decoder with initial states from the encoder.
- Concatenates Context Vector with Decoder Output before final classification.
- Employs Dense Layers for Prediction in a sequence-to-sequence task.
Hence, incorporating attention in an Encoder-Decoder architecture enhances sequence modeling by dynamically focusing on important encoder representations during decoding.