code: def _ranking_fast(
      context_hidden: torch.FloatTensor,
      next_hidden: torch.FloatTensor,
      next_top_k_probs: torch.FloatTensor,
      alpha: float,
      beam_width: int,
  ) -> torch.FloatTensor:
      """
      Reranks the top_k candidates based on a degeneration penalty (cosine similarity with previous tokens), as described
      in the paper "A Contrastive Framework for Neural Text Generation". Returns the index of the best candidate for each
      row in the batch.
      """
      norm_context_hidden = context_hidden / context_hidden.norm(
          dim=2, keepdim=True
      )
      norm_next_hidden = next_hidden / next_hidden.norm(
          dim=2, keepdim=True
      )
      cosine_matrix = torch.matmul(
          norm_context_hidden, norm_next_hidden.transpose(1, 2)
      ).squeeze(
          -1
      )  # [B*K, S]
      degeneration_penalty, _ = torch.max(
          cosine_matrix, dim=-1
      )  # [B*K]
      next_top_k_probs = next_top_k_probs.view(-1)  # [B*K]
      contrastive_score = (
          1.0 - alpha
      ) * next_top_k_probs - alpha * degeneration_penalty
      contrastive_score = torch.stack(
          torch.split(contrastive_score, beam_width)
      )  # [B, K]
      _, selected_idx = contrastive_score.max(dim=-1)  # [B]
      return selected_idx
  
imports: import inspect
  import warnings
  from dataclasses import dataclass
  from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
  import torch
  import torch.distributed
  from torch import nn
  from flexgen.timer import timers
  from .generation_beam_constraints import Constraint, DisjunctiveConstraint, PhrasalConstraint
  from .generation_beam_search import BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
  from .generation_logits_process import EncoderNoRepeatNGramLogitsProcessor, ExponentialDecayLengthPenalty, ForcedBOSTokenLogitsProcessor, ForcedEOSTokenLogitsProcessor, ForceTokensLogitsProcessor, HammingDiversityLogitsProcessor, InfNanRemoveLogitsProcessor, LogitNormalization, LogitsProcessorList, MinLengthLogitsProcessor, NoBadWordsLogitsProcessor, NoRepeatNGramLogitsProcessor, PrefixConstrainedLogitsProcessor, RepetitionPenaltyLogitsProcessor, SuppressTokensAtBeginLogitsProcessor, SuppressTokensLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TypicalLogitsWarper
  from .generation_stopping_criteria import MaxLengthCriteria, MaxTimeCriteria, StoppingCriteria, StoppingCriteriaList, validate_stopping_criteria
  from .modeling_outputs import CausalLMOutputWithPast, Seq2SeqLMOutput
  from .models.auto import MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, MODEL_FOR_VISION_2_SEQ_MAPPING
  from .pytorch_utils import torch_int_div
  from .utils import ModelOutput, logging
label_desc: Use image node_id 3 for calling a global function with example usage: _ranking_fast(context_hidden, next_hidden, next_top_k_probs, alpha, beam_width) and returns: selected_idx