code: def training_step(self, batch, batch_idx):
      batch = self.batch_dict(batch)
      mel = batch["log_mel"]
      speaker_ids = batch["speaker_id"]
      text = batch["text"]
      in_lens = batch["text_lens"]
      out_lens = batch["log_mel_lens"]
      attn_prior = batch["align_prior_matrix"]
      f0 = batch["pitch"]
      voiced_mask = batch["voiced_mask"]
      energy_avg = batch["energy"]
  
      if (
          self.train_config.binarization_start_iter >= 0
          and self.global_step
          >= self.train_config.binarization_start_iter
      ):
          # binarization training phase
          binarize = True
      else:
          # no binarization, soft-only
          binarize = False
  
      outputs = self.model(
          mel,
          speaker_ids,
          text,
          in_lens,
          out_lens,
          binarize_attention=binarize,
          attn_prior=attn_prior,
          f0=f0,
          energy_avg=energy_avg,
          voiced_mask=voiced_mask,
      )
      loss_outputs = self.criterion(outputs, in_lens, out_lens)
  
      loss = None
      for k, (v, w) in loss_outputs.items():
          if w > 0:
              loss = v * w if loss is None else loss + v * w
  
      if (
          binarize
          and self.global_step >= self.train_config.kl_loss_start_iter
      ):
          binarization_loss = self.attention_kl_loss(
              outputs["attn"], outputs["attn_soft"]
          )
          loss += binarization_loss
      else:
          binarization_loss = torch.zeros_like(loss)
      loss_outputs["binarization_loss"] = (binarization_loss, 1.0)
  
      for k, (v, w) in loss_outputs.items():
          self.log("train/" + k, loss_outputs[k][0], on_step=True)
  
      return {"loss": loss}
  
imports: import contextlib
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from pytorch_lightning.loggers import TensorBoardLogger
  from nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers import BaseTokenizer
  from nemo.collections.tts.losses.radttsloss import AttentionBinarizationLoss, RADTTSLoss
  from nemo.collections.tts.models.base import SpectrogramGenerator
  from nemo.collections.tts.parts.utils.helpers import batch_from_ragged, g2p_backward_compatible_support, plot_alignment_to_numpy, regulate_len, sample_tts_input
  from nemo.core.classes import Exportable
  from nemo.core.classes.common import typecheck
  from nemo.core.neural_types.elements import Index, MelSpectrogramType, RegressionValuesType, TokenDurationType, TokenIndex
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.core.optim.radam import RAdam
  from nemo.utils import logging
  from nemo.utils.decorators import experimental
label_desc: Use image node_id 3 for calling the RadTTSModel obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: dict