code: def forward(self, model_output, in_lens, out_lens):
      loss_dict = {}
      if len(model_output["z_mel"]):
          n_elements = out_lens.sum() // self.n_group_size
          mask = get_mask_from_lengths(out_lens // self.n_group_size)
          mask = mask[:, None].float()
          n_dims = model_output["z_mel"].size(1)
          loss_mel, loss_prior_mel = compute_flow_loss(
              model_output["z_mel"],
              model_output["log_det_W_list"],
              model_output["log_s_list"],
              n_elements,
              n_dims,
              mask,
              self.sigma,
          )
          loss_dict["loss_mel"] = (loss_mel, 1.0)  # loss, weight
          loss_dict["loss_prior_mel"] = (loss_prior_mel, 0.0)
  
      ctc_cost = self.attn_ctc_loss(
          attn_logprob=model_output["attn_logprob"],
          in_lens=in_lens,
          out_lens=out_lens,
      )
      loss_dict["loss_ctc"] = (
          ctc_cost,
          self.loss_weights["ctc_loss_weight"],
      )
  
      for k in model_output:
          if k in self.loss_fns:
              if (
                  model_output[k] is not None
                  and len(model_output[k]) > 0
              ):
                  t_lens = in_lens if "dur" in k else out_lens
                  mout = model_output[k]
                  for loss_name, v in self.loss_fns[k](
                      mout, t_lens
                  ).items():
                      loss_dict[loss_name] = v
  
      return loss_dict
  
file_path: NeMo/nemo/collections/tts/losses/radttsloss.py
imports: import torch
  from torch.nn import functional
  from nemo.collections.tts.losses.aligner_loss import ForwardSumLoss
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths
  from nemo.core.classes import Loss
label_desc: Use image node_id 2 for calling the RADTTSLoss obj's underlying member method code with example usage: obj.forward(model_output, in_lens, out_lens) and returns: loss_dict