code: def __call__(
      self, input_ids: torch.LongTensor, scores: torch.FloatTensor
  ) -> torch.FloatTensor:
      # calculate entropy
      normalized = torch.nn.functional.log_softmax(scores, dim=-1)
      p = torch.exp(normalized)
      ent = -(normalized * p).nansum(-1, keepdim=True)
  
      # shift and sort
      shifted_scores = torch.abs((-normalized) - ent)
      sorted_scores, sorted_indices = torch.sort(
          shifted_scores, descending=False
      )
      sorted_logits = scores.gather(-1, sorted_indices)
      cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
  
      # Remove tokens with cumulative mass above the threshold
      last_ind = (cumulative_probs < self.mass).sum(dim=1)
      last_ind[last_ind < 0] = 0
      sorted_indices_to_remove = sorted_scores > sorted_scores.gather(
          1, last_ind.view(-1, 1)
      )
      if self.min_tokens_to_keep > 1:
          # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
          sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
      indices_to_remove = sorted_indices_to_remove.scatter(
          1, sorted_indices, sorted_indices_to_remove
      )
  
      scores = scores.masked_fill(indices_to_remove, self.filter_value)
      return scores
  
imports: import functools
  import math
  import os
  import fsspec
  import torch
  import torch.nn
  import torch.nn.functional
  import torchaudio
  from transformers import LogitsWarper
  from TTS.tts.layers.tortoise.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
label_desc: Use image node_id 2 for calling the TypicalLogitsWarper obj's underlying member method code with example usage: obj.__call__(input_ids, scores) and returns: scores