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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 _tta_only(learn:Learner, ds_type:DatasetType=DatasetType.Valid, scale:float=1.35) -> Iterator[List[Tensor]]:
    "Computes the outputs for several augmented inputs for TTA"
    dl = learn.dl(ds_type)
    ds = dl.dataset
    old = ds.tfms
    augm_tfm = [o for o in learn.data.train_ds.tfms if o.tfm not in
               (crop_pad, flip_lr, dihedral, zoom)]
    try:
        pbar = master_bar(range(8))
        for i in pbar:
            row = 1 if i&1 else 0
            col = 1 if i&2 else 0
            flip = i&4
            d = {'row_pct':row, 'col_pct':col, 'is_random':False}
            tfm = [*augm_tfm, zoom(scale=scale, **d), crop_pad(**d)]
            if flip: tfm.append(flip_lr(p=1.))
            ds.tfms = tfm
            yield get_preds(learn.model, dl, pbar=pbar, activ=_loss_func2activ(learn.loss_func))[0]
    finally: ds.tfms = old