Transformers are a powerful model in modern machine learning, particularly in Natural Language Processing (NLP) tasks such as language translation and text summarization. They have revolutionized the field by replacing Long Short-Term Memory (LSTM) networks due to their ability to handle long-range dependencies and parallel computations. At the heart of Transformers is the attention mechanism, specifically the concept of ‘self-attention,’ which allows the model to weigh and prioritize different parts of the input data. This mechanism is what enables Transformers to manage long-range dependencies in data. It is fundamentally a weighting scheme that allows a model to focus on different parts of the input when producing an output. This mechanism allows the model to consider different words or features in the input sequence, assigning each one a ‘weight’ that signifies its importance for producing a given output. Transformer Implementation Steps  Setting up PyTorch: Before diving into b