class_docstr: This class implements the imperceptible, robust, and targeted attack to generate adversarial examples for automatic
  speech recognition models. This attack will be implemented specifically for DeepSpeech model and is framework
  dependent, specifically for PyTorch.
  
  | Paper link: https://arxiv.org/abs/1903.10346
docstr: The forward pass of the second stage of the attack.
  
  :param local_delta_rescale: Local delta after rescaled.
  :param theta_batch: Original thresholds.
  :param original_max_psd_batch: Original maximum psd.
  :param real_lengths: Real lengths of original sequences.
  :return: The loss tensor of the second stage of the attack.
docstr_tok: ["The","forward","pass","of","the","second","stage","of","the","attack",".",":","param","local_delta_rescale",":","Local","delta","after","rescaled",".",":","param","theta_batch",":","Original","thresholds",".",":","param","original_max_psd_batch",":","Original","maximum","psd",".",":","param","real_lengths",":","Real","lengths","of","original","sequences",".",":","return",":","The","loss","tensor","of","the","second","stage","of","the","attack","."]
code: def _forward_2nd_stage(
      self,
      local_delta_rescale: "torch.Tensor",
      theta_batch: List[np.ndarray],
      original_max_psd_batch: List[np.ndarray],
      real_lengths: np.ndarray,
  ) -> "torch.Tensor":
      """
      The forward pass of the second stage of the attack.
  
      :param local_delta_rescale: Local delta after rescaled.
      :param theta_batch: Original thresholds.
      :param original_max_psd_batch: Original maximum psd.
      :param real_lengths: Real lengths of original sequences.
      :return: The loss tensor of the second stage of the attack.
      """
      import torch
  
      # Compute loss for masking threshold
      losses = []
      relu = torch.nn.ReLU()
  
      for i, _ in enumerate(theta_batch):
          psd_transform_delta = self._psd_transform(
              delta=local_delta_rescale[i, : real_lengths[i]],
              original_max_psd=original_max_psd_batch[i],
          )
  
          loss = torch.mean(
              relu(
                  psd_transform_delta
                  - torch.tensor(theta_batch[i]).to(
                      self.estimator.device
                  )
              )
          )
          losses.append(loss)
  
      losses_stack = torch.stack(losses)
  
      return losses_stack
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import TYPE_CHECKING, Optional, Tuple, List
  import numpy
  import scipy
  from art.attacks.attack import EvasionAttack
  from art.estimators.pytorch import PyTorchEstimator
  from art.estimators.speech_recognition.pytorch_deep_speech import PyTorchDeepSpeech
  from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin
  from art.estimators.speech_recognition.speech_recognizer import PytorchSpeechRecognizerMixin
label_desc: Use image node_id 7 for calling the ImperceptibleASRPyTorch obj's underlying member method code with example usage: obj._forward_2nd_stage(local_delta_rescale, theta_batch, original_max_psd_batch, real_lengths) and returns: losses_stack