class_docstr: Module that holds a temperature as a learnable value.
  
  Args:
      initial_log_temperature (float): Initial value of log(temperature).
class_docstr_tok: ["Module","that","holds","a","temperature","as","a","learnable","value",".","Args",":","initial_log_temperature","(","float",")",":","Initial","value","of","log","(","temperature",")","."]
code: def __init__(self, initial_log_temperature=0):
      super().__init__()
      self.log_temperature = nn.Parameter(
          torch.tensor(initial_log_temperature, dtype=torch.float32)
      )
  
imports: import collections
  import copy
  from logging import getLogger
  import numpy
  import torch
  from torch import nn
  from torch.nn import functional
  import pfrl
  from pfrl.agent import AttributeSavingMixin, BatchAgent
  from pfrl.replay_buffer import ReplayUpdater, batch_experiences
  from pfrl.utils import clip_l2_grad_norm_
  from pfrl.utils.batch_states import batch_states
  from pfrl.utils.copy_param import synchronize_parameters
  from pfrl.utils.mode_of_distribution import mode_of_distribution
label_desc: Use image node_id 1 to create a new TemperatureHolder object from inherited base classes: nn with example: obj = TemperatureHolder(initial_log_temperature)