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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 res_block(nf, dense:bool=False, norm_type:Optional[NormType]=NormType.Batch, bottle:bool=False, **conv_kwargs):
    "Resnet block of `nf` features. `conv_kwargs` are passed to `conv_layer`."
    norm2 = norm_type
    if not dense and (norm_type==NormType.Batch): norm2 = NormType.BatchZero
    nf_inner = nf//2 if bottle else nf
    return SequentialEx(conv_layer(nf, nf_inner, norm_type=norm_type, **conv_kwargs),
                      conv_layer(nf_inner, nf, norm_type=norm2, **conv_kwargs),
                      MergeLayer(dense))