def params_size(m: Union[nn.Module,Learner], size: tuple = (3, 64, 64))->Tuple[Sizes, Tensor, Hooks]:
    "Pass a dummy input through the model to get the various sizes. Returns (res,x,hooks) if `full`"
    if isinstance(m, Learner):
        if m.data.is_empty:
            raise Exception("This is an empty `Learner` and `Learner.summary` requires some data to pass through the model.")
        ds_type = DatasetType.Train if m.data.train_dl else (DatasetType.Valid if m.data.valid_dl else DatasetType.Test)
        x = m.data.one_batch(ds_type=ds_type, detach=False, denorm=False)[0]
        x = [o[:1] for o in x]  if is_listy(x) else x[:1]
        m = m.model
    elif isinstance(m, nn.Module): x = next(m.parameters()).new(1, *size)
    else: raise TypeError('You should either pass in a Learner or nn.Module')
    with hook_outputs(flatten_model(m)) as hook_o:
        with hook_params(flatten_model(m))as hook_p:
            x = m.eval()(*x) if is_listy(x) else m.eval()(x)
            output_size = [((o.stored.shape[1:]) if o.stored is not None else None) for o in hook_o]
            params = [(o.stored if o.stored is not None else (None,None)) for o in hook_p]
    params, trainables = map(list,zip(*params))
    return output_size, params, trainables
Pass a dummy input through the model to get the various sizes. Returns (res,x,hooks) if `full`