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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 beam_search(self, text:str, n_words:int, no_unk:bool=True, top_k:int=10, beam_sz:int=1000, temperature:float=1.,
                    sep:str=' ', decoder=decode_spec_tokens):
        "Return the `n_words` that come after `text` using beam search."
        ds = self.data.single_dl.dataset
        self.model.reset()
        xb, yb = self.data.one_item(text)
        nodes = None
        xb = xb.repeat(top_k, 1)
        nodes = xb.clone()
        scores = xb.new_zeros(1).float()
        with torch.no_grad():
            for k in progress_bar(range(n_words), leave=False):
                out = F.log_softmax(self.model(xb)[0][:,-1], dim=-1)
                if no_unk: out[:,self.data.vocab.stoi[UNK]] = -float('Inf')
                values, indices = out.topk(top_k, dim=-1)
                scores = (-values + scores[:,None]).view(-1)
                indices_idx = torch.arange(0,nodes.size(0))[:,None].expand(nodes.size(0), top_k).contiguous().view(-1)
                sort_idx = scores.argsort()[:beam_sz]
                scores = scores[sort_idx]
                nodes = torch.cat([nodes[:,None].expand(nodes.size(0),top_k,nodes.size(1)),
                                indices[:,:,None].expand(nodes.size(0),top_k,1),], dim=2)
                nodes = nodes.view(-1, nodes.size(2))[sort_idx]
                self.model[0].select_hidden(indices_idx[sort_idx])
                xb = nodes[:,-1][:,None]
        if temperature != 1.: scores.div_(temperature)
        node_idx = torch.multinomial(torch.exp(-scores), 1).item()
        return text + sep + sep.join(decoder(self.data.vocab.textify([i.item() for i in nodes[node_idx][1:] ], sep=None)))