code: def teacher_force_everything(
      args, dataset, model, criterion, tgt_dict, rank, world_size
  ):
      prefix = args.prefix_length
  
      f0_decoder = None
      if args.dequantize_prosody:
          assert dataset.discrete_f0
          print("Reporting MAE for a discrete model")
          f0_decoder = Naive_F0_Decoder(
              args.f0_discretization_bounds,
              dataset.config.f0_vq_n_units,
          ).cuda()
  
      dataset = InferenceDataset(
          dataset,
          prefix=args.prefix_length,
          only_prefix=False,
          filter_short=True,
          presort_by_length=True,
      )
      sampler = (
          None
          if world_size == 1
          else DistributedSampler(
              dataset, num_replicas=world_size, rank=rank, shuffle=False
          )
      )
      dataloader = DataLoader(
          dataset,
          args.batch_size,
          shuffle=False,
          collate_fn=dataset.collater,
          sampler=sampler,
      )
  
      (
          total_token_loss,
          total_duration_loss,
          total_f0_loss,
          total_tokens,
      ) = (
          0.0,
          0.0,
          0.0,
          0.0,
      )
  
      i = 0
      for batch in dataloader:
          i += 1
          batch = move_to_cuda(batch)
          output = model(**batch["net_input"])
  
          tokens, durations, f0 = (
              output["token"],
              output["duration"],
              output["f0"],
          )
          durations, f0 = durations.squeeze(), f0.squeeze()
  
          token_loss = nll_loss(
              tokens[:, prefix - 1 :],
              batch["target"][:, prefix - 1 :].contiguous(),
              batch["mask"][:, prefix - 1 :].contiguous(),
              reduce=True,
          )
  
          if args.dequantize_prosody:
              durations = durations.argmax(dim=-1)
              duration_loss = mae_loss(
                  durations[:, prefix - 1 :].contiguous().float(),
                  batch["dur_target"][:, prefix - 1 :]
                  .contiguous()
                  .float(),
                  batch["dur_mask"][:, prefix - 1 :].contiguous(),
                  reduce=True,
              )
          else:
              duration_loss = criterion.dur_loss_fn(
                  durations[:, prefix - 1 :].contiguous(),
                  batch["dur_target"][:, prefix - 1 :].contiguous(),
                  batch["dur_mask"][:, prefix - 1 :].contiguous(),
                  reduce=True,
              )
  
          if f0_decoder:
              f0 = f0.argmax(dim=-1)
              f0 = f0_decoder(f0).squeeze(-1)
  
              f0_target = batch["raw_f0"]
              f0_loss = mae_loss(
                  f0[:, prefix - 1 :].contiguous(),
                  f0_target[:, prefix - 1 :].contiguous(),
                  batch["f0_mask"][:, prefix - 1 :].contiguous(),
                  reduce=True,
              )
          else:
              f0_loss = criterion.f0_loss_fn(
                  f0[:, prefix - 1 :].contiguous(),
                  batch["f0_target"][:, prefix - 1 :].contiguous(),
                  batch["f0_mask"][:, prefix - 1 :].contiguous(),
                  reduce=True,
              )
  
          n_tokens = (~batch["dur_mask"])[:, prefix - 1 :].sum()
  
          total_token_loss += token_loss.item()
          total_duration_loss += duration_loss.item()
          total_f0_loss += f0_loss.item()
  
          total_tokens += n_tokens.item()
          if args.debug and i > 5:
              break
  
      values = torch.tensor(
          [total_token_loss, total_duration_loss, total_f0_loss]
      )
      normalizers = torch.tensor([total_tokens for _ in range(3)])
  
      return values, normalizers
  
imports: import os
  import numpy
  import scipy
  import torch
  import torch.multiprocessing
  from fairseq import checkpoint_utils, options
  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
  from fairseq import utils
  from fairseq.criterions.speech_ulm_criterion import nll_loss, mae_loss
  import time
  from types import SimpleNamespace
  import sys, pathlib
  from naive_decoder import Naive_F0_Decoder
  from inference_dataset import InferenceDataset, explode_batch
  from sample.sample import do_sampling, TemperatureDecoder, FilterNamesDataset
label_desc: Use image node_id 1 for calling a global function with example usage: teacher_force_everything(args, dataset, model, criterion, tgt_dict, rank, world_size) and returns: values,  normalizers