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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 fit(epochs:int, learn:BasicLearner, callbacks:Optional[CallbackList]=None, metrics:OptMetrics=None)->None: "Fit the `model` on `data` and learn using `loss_func` and `opt`." assert len(learn.data.train_dl) != 0, f"""Your training dataloader is empty, can't train a model. Use a smaller batch size (batch size={learn.data.train_dl.batch_size} for {len(learn.data.train_dl.dataset)} elements)."""

    cb_handler = CallbackHandler(callbacks, metrics)
    pbar = master_bar(range(epochs))
    cb_handler.on_train_begin(epochs, pbar=pbar, metrics=metrics)

    exception=False
    try:
        for epoch in pbar:
            learn.model.train()
            cb_handler.set_dl(learn.data.train_dl)
            cb_handler.on_epoch_begin()
            for xb,yb in progress_bar(learn.data.train_dl, parent=pbar):
                xb, yb = cb_handler.on_batch_begin(xb, yb)
                loss = loss_batch(learn.model, xb, yb, learn.loss_func, learn.opt, cb_handler)
                if cb_handler.on_batch_end(loss): break

            if not cb_handler.skip_validate and not learn.data.empty_val:
                val_loss = validate(learn.model, learn.data.valid_dl, loss_func=learn.loss_func,
                                       cb_handler=cb_handler, pbar=pbar)
            else: val_loss=None
            if cb_handler.on_epoch_end(val_loss): break
    except Exception as e:
        exception = e
        raise
    finally: cb_handler.on_train_end(exception)