code: def __call__(self, output):
      def sample_multinomial(key, T):
          logits = output[key][:, -1, :].float()
          return Categorical(logits=logits / T).sample().unsqueeze(-1)
  
      def sample_laplace(key, T, truncate_at_zero):
          mean = output[key][:, -1, :].float()
          return truncated_laplace(
              mean=mean, T=T, truncate_by_zero=truncate_at_zero
          )
  
      if self.T_token > 0:
          new_tokens = sample_multinomial("token", self.T_token)
      else:
          new_tokens = output["token"][:, -1, :].argmax(
              dim=-1, keepdim=True
          )
  
      if not self.discrete_dur and self.T_dur == 0:
          new_durations = output["duration"][:, -1].round().int()
      elif not self.discrete_dur and self.T_dur > 0:
          new_durations = (
              sample_laplace(
                  "duration", self.T_dur, truncate_at_zero=True
              )
              .round()
              .int()
          )
      elif self.discrete_dur and self.T_dur > 0:
          new_durations = sample_multinomial("duration", self.T_dur)
      elif self.discrete_dur and self.T_dur == 0:
          new_durations = output["duration"][:, -1, :].argmax(
              dim=-1, keepdim=True
          )
      else:
          assert False
  
      if not self.discrete_f0 and self.T_f0 == 0:
          new_f0 = output["f0"][:, -1]
      elif not self.discrete_f0 and self.T_f0 > 0:
          new_f0 = sample_laplace(
              "f0", self.T_f0, truncate_at_zero=False
          )
      elif self.discrete_f0 and self.T_f0 > 0:
          new_f0 = sample_multinomial("f0", self.T_f0)
      elif self.discrete_f0 and self.T_f0 == 0:
          new_f0 = output["f0"][:, -1, :].argmax(dim=-1, keepdim=True)
      else:
          assert False
  
      return new_tokens, new_durations, new_f0
  
imports: import os
  import torch.multiprocessing
  import numpy
  import json
  import torch
  from torch.distributions.categorical import Categorical
  from fairseq import checkpoint_utils, options, utils
  from fairseq.data.codedataset import CodeDataset, ExpressiveCodeDataConfig
  from fairseq.dataclass.utils import convert_namespace_to_omegaconf
  from torch.utils.data import DataLoader, DistributedSampler
  from fairseq.utils import move_to_cuda
  import tqdm
  import random
  import pathlib
  import sys, pathlib
  from inference_dataset import InferenceDataset, explode_batch
  from naive_decoder import Naive_F0_Decoder
  from truncated_laplace import truncated_laplace
label_desc: Use image node_id 2 for calling the TemperatureDecoder obj's underlying member method code with example usage: obj.__call__(output) and returns: new_tokens,  new_durations,  new_f0, Categorical, truncated_laplace