code: def forward(self, hard_attention, soft_attention):
      log_sum = torch.log(soft_attention[hard_attention == 1]).sum()
      return -log_sum / hard_attention.sum()
  
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 AttentionBinarizationLoss obj's underlying member method code with example usage: obj.forward(hard_attention, soft_attention) and returns: unknown