class_docstr: This class implements a model-specific automatic speech recognizer using the end-to-end speech recognizer in
  Espresso.
  
  | Paper link: https://arxiv.org/abs/1909.08723
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docstr: Compute the gradient of the loss function w.r.t. `x`.
  
  :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
            could have different lengths. A possible example of `x` could be:
            `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
  :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
            lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
  :return: Loss gradients of the same shape as `x`.
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code: def loss_gradient(
      self, x: np.ndarray, y: np.ndarray, **kwargs
  ) -> np.ndarray:
      """
      Compute the gradient of the loss function w.r.t. `x`.
  
      :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
                could have different lengths. A possible example of `x` could be:
                `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
      :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
                lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
      :return: Loss gradients of the same shape as `x`.
      """
      x_in = np.empty(len(x), dtype=object)
      x_in[:] = list(x)
  
      # Put the model in the training mode, otherwise CUDA can't backpropagate through the model.
      # However, model uses batch norm layers which need to be frozen
      self.model.train()
      self.set_batchnorm(train=False)
  
      # Apply preprocessing
      x_preprocessed, y_preprocessed = self._apply_preprocessing(
          x_in, y, fit=False
      )
  
      # Transform data into the model input space
      batch_dict, batch_idx = self._transform_model_input(
          x=x_preprocessed, y=y_preprocessed, compute_gradient=True
      )
  
      loss, _, _ = self.criterion(self.model, batch_dict)
      loss.backward()
  
      # Get results
      results_list = []
      src_frames = (
          batch_dict["net_input"]["src_tokens"]
          .grad.cpu()
          .numpy()
          .copy()
      )
      src_lengths = (
          batch_dict["net_input"]["src_lengths"].cpu().numpy().copy()
      )
      for i, _ in enumerate(x_preprocessed):
          results_list.append(src_frames[i, : src_lengths[i], :])
  
      results = np.array(results_list)
  
      if results.shape[0] == 1:
          results_ = np.empty(len(results), dtype=object)
          results_[:] = list(results)
          results = results_
  
      # Rearrange to the original order
      results_ = results.copy()
      results[batch_idx] = results_
  
      results = self._apply_preprocessing_gradient(x_in, results)
  
      if x.dtype != object:
          results = np.array(
              [i for i in results], dtype=x.dtype
          )  # pylint: disable=R1721
          assert results.shape == x.shape and results.dtype == x.dtype
      else:
          results = np.array(
              [np.squeeze(res) for res in results], dtype=object
          )
  
      # Unfreeze batch norm layers again
      self.set_batchnorm(train=True)
  
      return results
  
imports: import ast
  from argparse import Namespace
  import logging
  from typing import Dict, List, Optional, Tuple, TYPE_CHECKING, Union
  import numpy
  from art import config
  from art.estimators.pytorch import PyTorchEstimator
  from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin, PytorchSpeechRecognizerMixin
  from art.utils import get_file
label_desc: Use image node_id 3 for calling the PyTorchEspresso obj's underlying member method code with example usage: obj.loss_gradient(x, y) and returns: results