In the PyTorch library, the data loaders are used to provide an interface where common operations such as batching can be implemented. It is also
possible to parallelize the data loading process by using multiple worker processes. This can improve performance by increasing the number of batches
being fetched in parallel, at the cost of higher memory usage. This performance increase can also be attributed to avoiding the Global Interpreter
Lock (GIL) in the Python interpreter.
