class_docstr: Implementation of the ObjectSeeker certifiable robust defense applied to object detection models.
  The original implementation is https://github.com/inspire-group/ObjectSeeker
  
  | Paper link: https://arxiv.org/abs/2202.01811
class_docstr_tok: ["Implementation","of","the","ObjectSeeker","certifiable","robust","defense","applied","to","object","detection","models",".","The","original","implementation","is","https",":","\/\/github.com\/inspire-group\/ObjectSeeker","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/2202.01811"]
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,
      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`.
      """
      return self.detector.get_activations(
          x=x,
          layer=layer,
          batch_size=batch_size,
          framework=framework,
      )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import sys
  from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from art.estimators.certification.object_seeker.object_seeker import ObjectSeekerMixin
  from art.estimators.object_detection import ObjectDetectorMixin, PyTorchObjectDetector, PyTorchFasterRCNN, PyTorchYolo
  from art.estimators.pytorch import PyTorchEstimator
label_desc: Use image node_id 12 for calling the PyTorchObjectSeeker obj's underlying member method code with example usage: obj.get_activations(x, layer, batch_size, framework) and returns: self