class_docstr: Efficiently Trainable Text-to-Speech System Based on Deep Convolutional
  Networks with Guided Attention (https://arxiv.org/abs/1710.08969)
class_docstr_tok: ["Efficiently","Trainable","Text-to-Speech","System","Based","on","Deep","Convolutional","Networks","with","Guided","Attention","(","https",":","\/\/arxiv.org\/abs\/1710.08969",")"]
code: def __init__(self, sigma):
      super().__init__()
      self.sigma = sigma
  
imports: import logging
  from dataclasses import dataclass, field
  from functools import lru_cache
  from typing import Any, Dict, List
  import torch
  import torch.nn.functional
  from omegaconf import II
  from fairseq import utils
  from fairseq.logging import metrics
  from fairseq.criterions import FairseqCriterion, register_criterion
  from fairseq.data.data_utils import lengths_to_mask
  from fairseq.dataclass import FairseqDataclass
label_desc: Use image node_id 1 to create a new GuidedAttentionLoss object from inherited base classes: torch.nn with example: obj = GuidedAttentionLoss(sigma)