Suppose we have a simple MLP network to classify MNIST (similar to hogwild mnist example). If our goal is not to train the model asynchronously (like in the example) but to train different models each with a distinct random seeds. What is the best way to use torch.multiprocessing.Process  ? Since we don’t share_memory across models when calling the worker function ?