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",")","."]
docstr: Return a temperature as a torch.Tensor.
docstr_tok: ["Return","a","temperature","as","a","torch.Tensor","."]
code: def forward(self):
      """Return a temperature as a torch.Tensor."""
      return torch.exp(self.log_temperature)
  
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 2 for calling the TemperatureHolder obj's underlying member method code with example usage: obj.forward() and returns: torch