class_docstr: This class implements a classifier with the HuggingFace framework.
docstr: Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
  `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
  calling `layer_names`.
  
  :param x: Input for computing the activations.
  :param layer: Layer for computing the activations
  :param batch_size: Size of batches.
  :param framework: If true, return the intermediate tensor representation of the activation.
  :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
code: def get_activations(  # type: ignore
      self,
      x: Union[np.ndarray, "torch.Tensor"],
      layer: Optional[Union[int, str]] = None,
      batch_size: int = 128,
      framework: bool = False,
  ) -> Union[np.ndarray, "torch.Tensor"]:
      """
      Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
      `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
      calling `layer_names`.
  
      :param x: Input for computing the activations.
      :param layer: Layer for computing the activations
      :param batch_size: Size of batches.
      :param framework: If true, return the intermediate tensor representation of the activation.
      :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
      """
      import torch
  
      self._model.eval()
  
      # Apply defences
      if framework:
          no_grad = False
      else:
          no_grad = True
      x_preprocessed, _ = self._apply_preprocessing(
          x=x, y=None, fit=False, no_grad=no_grad
      )
  
      # Get index of the extracted layer
      if isinstance(layer, six.string_types):
          if layer not in self._layer_names:  # pragma: no cover
              raise ValueError(f"Layer name {layer} not supported")
          layer_index = self._layer_names.index(layer)
  
      elif isinstance(layer, int):
          layer_index = layer
  
      else:  # pragma: no cover
          raise TypeError("Layer must be of type str or int")
  
      def get_feature(name):
          # the hook signature
          def hook(model, input, output):  # pylint: disable=W0622,W0613
              # TODO: this is using the input, rather than the output, to circumvent the fact
              # TODO: that flatten is not a layer in pytorch, and the activation defence expects
              # TODO: a flattened input. A better option is to refactor the activation defence
              # TODO: to not crash if non 2D inputs are provided.
              self._features[name] = input
  
          return hook
  
      if not hasattr(self, "_features"):
          self._features: Dict[str, torch.Tensor] = {}
          # register forward hooks on the layers of choice
      handles = []
  
      lname = self._layer_names[layer_index]
  
      if layer not in self._features:
          for name, module in self.model.named_modules():
              if (
                  name == lname
                  and len(list(module.named_modules())) == 1
              ):
                  handles.append(
                      module.register_forward_hook(get_feature(name))
                  )
  
      if framework:
          if isinstance(x_preprocessed, torch.Tensor):
              self._model(x_preprocessed)
              return self._features[self._layer_names[layer_index]][0]
          input_tensor = torch.from_numpy(x_preprocessed)
          self._model(input_tensor.to(self._device))
          return self._features[self._layer_names[layer_index]][
              0
          ]  # pylint: disable=W0212
  
      # Run prediction with batch processing
      results = []
      num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
  
      for m in range(num_batch):
          # Batch indexes
          begin, end = (
              m * batch_size,
              min((m + 1) * batch_size, x_preprocessed.shape[0]),
          )
  
          # Run prediction for the current batch
          self._model(
              torch.from_numpy(x_preprocessed[begin:end]).to(
                  self._device
              )
          )
          layer_output = self._features[
              self._layer_names[layer_index]
          ]  # pylint: disable=W0212
  
          if isinstance(layer_output, tuple):
              results.append(layer_output[0].detach().cpu().numpy())
          else:
              results.append(layer_output.detach().cpu().numpy())
  
      results_array = np.concatenate(results)
      return results_array
  
file_path: adversarial-robustness-toolbox/art/estimators/classification/hugging_face.py
imports: import logging
  from typing import List, Optional, Tuple, Union, Dict, Callable, Any, TYPE_CHECKING
  import numpy
  import six
  from art.estimators.classification.pytorch import PyTorchClassifier
label_desc: Use image node_id 3 for calling the HuggingFaceClassifierPyTorch obj's underlying member method code with example usage: obj.get_activations(x, layer, batch_size, framework) and returns: results_array, hook, self, self