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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def validate(model:nn.Module, dl:DataLoader, loss_func:OptLossFunc=None, cb_handler:Optional[CallbackHandler]=None,
             pbar:Optional[PBar]=None, average=True, n_batch:Optional[int]=None)->Iterator[Tuple[Union[Tensor,int],...]]:
    "Calculate `loss_func` of `model` on `dl` in evaluation mode."
    model.eval()
    with torch.no_grad():
        val_losses,nums = [],[]
        if cb_handler: cb_handler.set_dl(dl)
        for xb,yb in progress_bar(dl, parent=pbar, leave=(pbar is not None)):
            if cb_handler: xb, yb = cb_handler.on_batch_begin(xb, yb, train=False)
            val_loss = loss_batch(model, xb, yb, loss_func, cb_handler=cb_handler)
            val_losses.append(val_loss)
            if not is_listy(yb): yb = [yb]
            nums.append(yb[0].shape[0])
            if cb_handler and cb_handler.on_batch_end(val_losses[-1]): break
            if n_batch and (len(nums)>=n_batch): break
        nums = np.array(nums, dtype=np.float32)
        if average: return (to_np(torch.stack(val_losses)) * nums).sum() / nums.sum()
        else:       return val_losses