You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: main.rs
    path of file: ./repos/candle/candle-examples/examples/whisper
    
    the code of the file until where you have to start completion: // https://github.com/openai/whisper/blob/main/whisper/model.py/rgs
// TODO:
// - Batch size greater than 1.
// - More token filters (SuppressBlanks, ApplyTimestampRules).

#[cfg(feature = "accelerate")
    
    fn decode(&mut self, mel: &Tensor, t: f64) -> Result<DecodingResult> {
        let model = &mut self.model;
        let audio_features = model.encoder_forward(mel, true)?;
        if self.verbose {
            println!("audio features: {:?}", audio_features.dims());
        }
        let sample_len = model.config().max_target_positions / 2;
        let mut sum_logprob = 0f64;
        let mut no_speech_prob = f64::NAN;
        let mut tokens = vec![self.sot_token];
        if let Some(language_token) = self.language_token {
            tokens.push(language_token);
        }
        match self.task {
            None | Some(Task::Transcribe) => tokens.push(self.transcribe_token),
            Some(Task::Translate) => tokens.push(self.translate_token),
        }
        if !self.timestamps {
            tokens.push(self.no_timestamps_token);
        }
        for i in 0..sample_len {
            let tokens_t = Tensor::new(tokens.as_slice(), mel.device())?;

            // The model expects a batch dim but this inference loop does not handle
            // it so we add it at this point.
            let tokens_t = tokens_t.unsqueeze(0)?;
            let ys = model.decoder_forward(&tokens_t, &audio_features, i == 0)?;

            // Extract the no speech probability on the first iteration by looking at the first
            // token logits and the probability for the according token.
            if i == 0 {
                let logits = model.decoder_final_linear(&ys.i(..1)?)?.i(0)?.i(0)?;
                no_speech_prob = softmax(&logits, 0)?
                    .i(self.no_speech_token as usize)?
                    .to_scalar::<f32>()? as f64;
            }

            let (_, seq_len, _) = ys.dims3()?;
            let logits = model
                .decoder_final_linear(&ys.i((..1, seq_len - 1..))?)?
                .i(0)?
                .i(0)?;
            // TODO: Besides suppress tokens, we should apply the heuristics from
            // ApplyTimestampRules, i.e.:
            // - Timestamps come in pairs, except before EOT.
            // - Timestamps should be non-decreasing.
            // - If the sum of the probabilities of timestamps is higher than any other tokens,
            //   only consider timestamps when sampling.
            // https://github.com/openai/whisper/blob/e8622f9afc4eba139bf796c210f5c01081000472/whisper/decoding.py#L439
            let logits = logits.broadcast_add(&self.suppress_tokens)?;
            let next_token = if t > 0f64 {
                let prs = softmax(&(&logits / t)?, 0)?;
                let logits_v: Vec<f32> = prs.to_vec1()?;
                let distr = rand::distributions::WeightedIndex::new(&logits_v)?;
                distr.sample(&mut self.rng) as u32
            } else {
                let logits_v: Vec<f32> = logits.to_vec1()?;
                logits_v
                    .iter()
                    .enumerate()
                    .max_by(|(_, u), (_, v)| u.total_cmp(v))
                    .map(|(i, _)| i as u32)
                    .unwrap()
            };
            tokens.push(next_token);
            let prob = softmax(&logits, candle::D::Minus1)?
                .i(next_token as usize)?
                .to_scalar::<f32>()? as f64;
            if next_token == self.eot_token || tokens.len() > model.config().max_target_positions {
                break;
            }
            sum_logprob += prob.ln();
        }
        let text = self.tokenizer.decode(&tokens, true).map_err(E::msg)?;
        let avg_logprob = sum_logprob / tokens.len() as f64;

        Ok(DecodingResult {
            tokens,
            text,
            avg_logprob,
            no_speech_prob,
            temperature: t,
            compression_ratio: f64::NAN,
        })
    }