In the context of deep learning, particularly when utilizing frameworks such as PyTorch, the concept of loss and its relationship with gradients and optimizers is fundamental. To address the question one needs to consider the mechanics of how neural networks learn and improve their performance through iterative optimization processes. When training a deep learning model,