class_docstr: [`LogitsWarper`] for temperature (exponential scaling output probability distribution).
  This version allows for a separate value for each sample and runs inplace when possible.
  It doesn't validate inputs.
  
  Args:
      temperature (`float`):
          The value used to module the logits distribution.
code: def __call__(
      self, input_ids: torch.Tensor, scores: torch.Tensor
  ) -> torch.Tensor:
      scores.div_(self.temperature_tensor)
      return scores
  
imports: import math
  import torch
  from functools import lru_cache
  from typing import Optional, List, Dict, Union
  from transformers import LogitsWarper, LogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TypicalLogitsWarper
label_desc: Use image node_id 2 for calling the HeterogeneousTemperatureLogitsWarper obj's underlying member method code with example usage: obj.__call__(input_ids, scores) and returns: scores