class_docstr: This class implements a classifier with the TensorFlow framework.
class_docstr_tok: ["This","class","implements","a","classifier","with","the","TensorFlow","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`.
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code: def get_activations(
      self,
      x: np.ndarray,
      layer: Union[int, str],
      batch_size: int = 128,
      framework: bool = False,
  ) -> np.ndarray:
      """
      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`.
      """
      # pylint: disable=E0401
      import tensorflow.compat.v1 as tf
  
      if self.learning is not None:
          self.feed_dict[self.learning] = False
  
      # Get the computational graph
      with self._sess.graph.as_default():
          graph = tf.get_default_graph()
  
      if isinstance(
          layer, six.string_types
      ):  # basestring for Python 2 (str, unicode) support
          if layer not in self._layer_names:  # pragma: no cover
              raise ValueError(
                  f"Layer name {layer} is not part of the graph."
              )
          layer_tensor = graph.get_tensor_by_name(layer)
  
      elif isinstance(layer, int):
          layer_tensor = graph.get_tensor_by_name(
              self._layer_names[layer]
          )
  
      else:  # pragma: no cover
          raise TypeError(
              f"Layer must be of type `str` or `int`. Received {layer}."
          )
  
      if framework:
          return layer_tensor
  
      # Apply preprocessing
      x_preprocessed, _ = self._apply_preprocessing(
          x, y=None, fit=False
      )
  
      # 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]),
          )
  
          # Create feed_dict
          feed_dict = {self.input_ph: x_preprocessed[begin:end]}
          feed_dict.update(self.feed_dict)
  
          # Run prediction for the current batch
          layer_output = self._sess.run(
              layer_tensor, feed_dict=feed_dict
          )
          results.append(layer_output)
  
      results_array = np.concatenate(results)
  
      return results_array
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  import random
  import shutil
  import time
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
  from tqdm.auto import tqdm
  import numpy
  import six
  from art import config
  from art.estimators.classification.classifier import ClassGradientsMixin, ClassifierMixin
  from art.estimators.tensorflow import TensorFlowEstimator, TensorFlowV2Estimator
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 19 for calling the TensorFlowClassifier obj's underlying member method code with example usage: obj.get_activations(x, layer, batch_size, framework) and returns: results_array, layer_tensor