For load_state_dict, the documentation states:
Whether you are loading from a partial  *state_dict* , which is missing some keys, or loading a  *state_dict*  with more keys than the model that you are loading into, you can set the  strict argument to  **False**  in the load_state_dict()  function to ignore non-matching keys. (from https://pytorch.org/tutorials/beginner/saving_loading_models.html#id4 199)
I have created a new model by augmenting the architecture slightly, doubling the number of trainable parameters (by introducing more modules), but I want to retain the parameters from a checkpoint (but keep them trainable for fine-tuning in my model) with a different dataset. I want to make sure that the newly introduced parameters will be trained, instead of ignored when I set strict=False, or if I should look into doing something else. Thanks!