class_docstr: Guided attention loss function module.
  
  This module calculates the guided attention loss described
  in `Efficiently Trainable Text-to-Speech System Based
  on Deep Convolutional Networks with Guided Attention`_,
  which forces the attention to be diagonal.
  
  .. _`Efficiently Trainable Text-to-Speech System
      Based on Deep Convolutional Networks with Guided Attention`:
      https://arxiv.org/abs/1710.08969
class_docstr_tok: ["Guided","attention","loss","function","module",".","This","module","calculates","the","guided","attention","loss","described","in","`","Efficiently","Trainable","Text-to-Speech","System","Based","on","Deep","Convolutional","Networks","with","Guided","Attention","`","_",",","which","forces","the","attention","to","be","diagonal",".","..","_","`","Efficiently","Trainable","Text-to-Speech","System","Based","on","Deep","Convolutional","Networks","with","Guided","Attention","`",":","https",":","\/\/arxiv.org\/abs\/1710.08969"]
code: def _reset_masks(self):
      self.guided_attn_masks = None
      self.masks = None
  
imports: import logging
  import numpy
  import torch
  import torch.nn.functional
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
  from espnet.nets.pytorch_backend.rnn.attentions import AttForward, AttForwardTA, AttLoc
  from espnet.nets.pytorch_backend.tacotron2.cbhg import CBHG, CBHGLoss
  from espnet.nets.pytorch_backend.tacotron2.decoder import Decoder
  from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder
  from espnet.nets.tts_interface import TTSInterface
  from espnet.utils.cli_utils import strtobool
  from espnet.utils.fill_missing_args import fill_missing_args
label_desc: Use image node_id 2 for calling the GuidedAttentionLoss obj's underlying member method code with example usage: obj._reset_masks() without return types