code: def __init__(self, name, model_config, loss_weight, sigma=1.0):
      super(AttributePredictionLoss, self).__init__()
      self.name = name
      self.sigma = sigma
      self.model_name = model_config["name"]
      self.loss_weight = loss_weight
      self.n_group_size = 1
      if "n_group_size" in model_config["hparams"]:
          self.n_group_size = model_config["hparams"]["n_group_size"]
  
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 1 to create a new AttributePredictionLoss object from inherited base classes: torch.nn with example: obj = AttributePredictionLoss(name, model_config, loss_weight, sigma)