Add support for batch processing

[frgfm]

Following up on #79 & #90, the CAM methods should be able to process batches. With the way they are computed, here is the proposal to handle two cases:

• if the model got an input_tensor with batch size = 1, then the CAM extractor can only be passed either a single integer (class index) or a list of class indices of length 1.
• if the model gets a tensor with batch_size = N, then:
  • if the CAM extractor receives class_idx: int, it will return a tensor of shape (N, H, W) which is the class activation map for class corresponding to index = class_idx, for each element of the input batch
  • if the CAM extractor receives class_idx: List[int] (of size N), it will return a tensor of shape (N, H, W) where element k, along the batch axis, will be the class activation map for class corresponding to index = class_idx[k] for the k-th element of the input batch.

So far, for more complex behaviours, I cannot foresee a way to do it efficiently (without breaking the backprop).