Some questions about meta learning theory

[1999kevin]

Hi, I'm a beginnger for meta-learning. As I know, it is used for fitting model across different data. In each epoch for meta-learning, there are a batch of tasks and we should make copies of model for each task. For example, to meta-learn the shapenet dataset, in each epoch, we select several cars to process, thus the dimension of dataflow should be [b, ...], while b is the batch size. So, we need to make b copies of basic model so that each model can fit a particular car. However, I find that you only make one copy for the model in the inner loop(copy.deepcopy). Could you please give an explanation here? Do I misunderstand it?