code: def run(self, mel: Tensor) -> List[DecodingResult]:
      self.decoder.reset()
      tokenizer: Tokenizer = self.tokenizer
      n_audio: int = mel.shape[0]
  
      audio_features: Tensor = self._get_audio_features(
          mel
      )  # encoder forward pass
      tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(
          n_audio, 1
      )
  
      # detect language if requested, overwriting the language token
      languages, language_probs = self._detect_language(
          audio_features, tokens
      )
      if self.options.task == "lang_id":
          return [
              DecodingResult(
                  audio_features=features,
                  language=language,
                  language_probs=probs,
              )
              for features, language, probs in zip(
                  audio_features, languages, language_probs
              )
          ]
  
      # repeat text tensors by the group size, for beam search or best-of-n sampling
      tokens = tokens.repeat_interleave(self.n_group, dim=0).to(
          audio_features.device
      )
  
      # call the main sampling loop
      tokens, sum_logprobs, no_speech_probs = self._main_loop(
          audio_features, tokens
      )
  
      # reshape the tensors to have (n_audio, n_group) as the first two dimensions
      audio_features = audio_features[:: self.n_group]
      no_speech_probs = no_speech_probs[:: self.n_group]
      assert audio_features.shape[0] == len(no_speech_probs) == n_audio
  
      tokens = tokens.reshape(n_audio, self.n_group, -1)
      sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group)
  
      # get the final candidates for each group, and slice between the first sampled token and EOT
      tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
      tokens: List[List[Tensor]] = [
          [
              t[
                  self.sample_begin : (t == tokenizer.eot).nonzero()[
                      0, 0
                  ]
              ]
              for t in s
          ]
          for s in tokens
      ]
  
      # select the top-ranked sample in each group
      selected = self.sequence_ranker.rank(tokens, sum_logprobs)
      tokens: List[List[int]] = [
          t[i].tolist() for i, t in zip(selected, tokens)
      ]
      texts: List[str] = [tokenizer.decode(t).strip() for t in tokens]
  
      sum_logprobs: List[float] = [
          lp[i] for i, lp in zip(selected, sum_logprobs)
      ]
      avg_logprobs: List[float] = [
          lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)
      ]
  
      fields = (
          texts,
          languages,
          tokens,
          audio_features,
          avg_logprobs,
          no_speech_probs,
      )
      if len(set(map(len, fields))) != 1:
          raise RuntimeError(
              f"inconsistent result lengths: {list(map(len, fields))}"
          )
  
      return [
          DecodingResult(
              audio_features=features,
              language=language,
              tokens=tokens,
              text=text,
              avg_logprob=avg_logprob,
              no_speech_prob=no_speech_prob,
              temperature=self.options.temperature,
              compression_ratio=compression_ratio(text),
          )
          for text, language, tokens, features, avg_logprob, no_speech_prob in zip(
              *fields
          )
      ]
  
imports: from dataclasses import dataclass, field, replace
  from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.distributions import Categorical
  from .audio import CHUNK_LENGTH
  from .tokenizer import Tokenizer, get_tokenizer
  from .utils import compression_ratio
label_desc: Use image node_id 8 for calling the DecodingTask obj's underlying member method code with example usage: obj.run(mel) and returns: unknown, unknown