class_docstr: [`TFLogitsWarper`] for temperature (exponential scaling output probability distribution).
  
  Args:
      temperature (`float`):
          The value used to module the logits distribution.
class_docstr_tok: ["[","`","TFLogitsWarper","`","]","for","temperature","(","exponential","scaling","output","probability","distribution",")",".","Args",":","temperature","(","`","float","`",")",":","The","value","used","to","module","the","logits","distribution","."]
code: def __init__(self, temperature: float):
      if not isinstance(temperature, float) or not (temperature > 0):
          raise ValueError(
              f"`temperature` has to be a strictly positive float, but is {temperature}"
          )
  
      self.temperature = temperature
  
imports: import inspect
  from typing import List, Tuple
  import numpy
  import tensorflow
  from ..tf_utils import stable_softmax
  from ..utils import add_start_docstrings
  from ..utils.logging import get_logger
label_desc: Use image node_id 1 to create a new TFTemperatureLogitsWarper object from inherited base classes: TFLogitsWarper with example: obj = TFTemperatureLogitsWarper(temperature)