class_docstr: A class containing all functions for auto-regressive text generation, to be used as a mixin in [`PreTrainedModel`].
  
  The class exposes [`~generation_utils.GenerationMixin.generate`], which can be used for:
      - *greedy decoding* by calling [`~generation_utils.GenerationMixin.greedy_search`] if `num_beams=1` and
        `do_sample=False`.
      - *contrastive search* by calling [`~generation_utils.GenerationMixin.contrastive_search`] if `penalty_alpha>0`
        and `top_k>1`
      - *multinomial sampling* by calling [`~generation_utils.GenerationMixin.sample`] if `num_beams=1` and
        `do_sample=True`.
      - *beam-search decoding* by calling [`~generation_utils.GenerationMixin.beam_search`] if `num_beams>1` and
        `do_sample=False`.
      - *beam-search multinomial sampling* by calling [`~generation_utils.GenerationMixin.beam_sample`] if
        `num_beams>1` and `do_sample=True`.
      - *diverse beam-search decoding* by calling [`~generation_utils.GenerationMixin.group_beam_search`], if
        `num_beams>1` and `num_beam_groups>1`.
      - *constrained beam-search decoding* by calling [`~generation_utils.GenerationMixin.constrained_beam_search`],
        if `constraints!=None` or `force_words_ids!=None`.
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docstr: Implement in subclasses of [`PreTrainedModel`] for custom behavior to adjust the logits in the generate method.
docstr_tok: ["Implement","in","subclasses","of","[","`","PreTrainedModel","`","]","for","custom","behavior","to","adjust","the","logits","in","the","generate","method","."]
code: def adjust_logits_during_generation(
      self, logits: torch.FloatTensor, **kwargs
  ) -> torch.FloatTensor:
      """
      Implement in subclasses of [`PreTrainedModel`] for custom behavior to adjust the logits in the generate method.
      """
      return logits
  
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 the GenerationMixin obj's underlying member method code with example usage: obj.adjust_logits_during_generation(logits) and returns: logits