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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 cnn_learner(data:DataBunch, base_arch:Callable, cut:Union[int,Callable]=None, pretrained:bool=True,
                lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5, custom_head:Optional[nn.Module]=None,
                split_on:Optional[SplitFuncOrIdxList]=None, bn_final:bool=False, init=nn.init.kaiming_normal_,
                concat_pool:bool=True, **kwargs:Any)->Learner:
    "Build convnet style learner."
    meta = cnn_config(base_arch)
    model = create_cnn_model(base_arch, data.c, cut, pretrained, lin_ftrs, ps=ps, custom_head=custom_head,
        split_on=split_on, bn_final=bn_final, concat_pool=concat_pool)
    learn = Learner(data, model, **kwargs)
    learn.split(split_on or meta['split'])
    if pretrained: learn.freeze()
    if init: apply_init(model[1], init)
    return learn