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: Perform prediction for a batch of inputs.
  
  :param x: Samples of shape NCHW or NHWC.
  :param batch_size: Batch size.
  :return: Predictions of format `List[Dict[str, np.ndarray]]`, one for each input image. The fields of the Dict
           are as follows:
  
           - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
           - labels [N]: the labels for each image
           - scores [N]: the scores or each prediction.
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code: def predict(
      self, x: np.ndarray, batch_size: int = 128, **kwargs
  ) -> List[Dict[str, np.ndarray]]:
      """
      Perform prediction for a batch of inputs.
  
      :param x: Samples of shape NCHW or NHWC.
      :param batch_size: Batch size.
      :return: Predictions of format `List[Dict[str, np.ndarray]]`, one for each input image. The fields of the Dict
               are as follows:
  
               - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
               - labels [N]: the labels for each image
               - scores [N]: the scores or each prediction.
      """
      return ObjectSeekerMixin.predict(
          self, x=x, batch_size=batch_size, **kwargs
      )
  
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 10 for calling the PyTorchObjectSeeker obj's underlying member method code with example usage: obj.predict(x, batch_size) and returns: ObjectSeekerMixin