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: Fit the classifier on the training set `(x, y)`.
  
  :param x: Samples of shape NCHW or NHWC.
  :param y: Target values of format `List[Dict[str, Union[np.ndarray, torch.Tensor]]]`, 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.
  :param batch_size: Size of batches.
  :param nb_epochs: Number of epochs to use for training.
  :param drop_last: Set to ``True`` to drop the last incomplete batch, if the dataset size is not divisible by
                    the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then
                    the last batch will be smaller. (default: ``False``)
  :param scheduler: Learning rate scheduler to run at the start of every epoch.
  :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
                 and providing it takes no effect.
docstr_tok: ["Fit","the","classifier","on","the","training","set","`","(","x",",","y",")","`",".",":","param","x",":","Samples","of","shape","NCHW","or","NHWC",".",":","param","y",":","Target","values","of","format","`","List","[","Dict","[","str",",","Union","[","np.ndarray",",","torch.Tensor","]","]","]","`",",","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",".",":","param","batch_size",":","Size","of","batches",".",":","param","nb_epochs",":","Number","of","epochs","to","use","for","training",".",":","param","drop_last",":","Set","to","``","True","``","to","drop","the","last","incomplete","batch",",","if","the","dataset","size","is","not","divisible","by","the","batch","size",".","If","``","False","``","and","the","size","of","dataset","is","not","divisible","by","the","batch","size",",","then","the","last","batch","will","be","smaller",".","(","default",":","``","False","``",")",":","param","scheduler",":","Learning","rate","scheduler","to","run","at","the","start","of","every","epoch",".",":","param","kwargs",":","Dictionary","of","framework-specific","arguments",".","This","parameter","is","not","currently","supported","for","PyTorch","and","providing","it","takes","no","effect","."]
code: def fit(  # pylint: disable=W0221
      self,
      x: np.ndarray,
      y: List[Dict[str, Union[np.ndarray, "torch.Tensor"]]],
      batch_size: int = 128,
      nb_epochs: int = 10,
      drop_last: bool = False,
      scheduler: Optional[
          "torch.optim.lr_scheduler._LRScheduler"
      ] = None,
      **kwargs,
  ) -> None:
      """
      Fit the classifier on the training set `(x, y)`.
  
      :param x: Samples of shape NCHW or NHWC.
      :param y: Target values of format `List[Dict[str, Union[np.ndarray, torch.Tensor]]]`, 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.
      :param batch_size: Size of batches.
      :param nb_epochs: Number of epochs to use for training.
      :param drop_last: Set to ``True`` to drop the last incomplete batch, if the dataset size is not divisible by
                        the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then
                        the last batch will be smaller. (default: ``False``)
      :param scheduler: Learning rate scheduler to run at the start of every epoch.
      :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
                     and providing it takes no effect.
      """
      self.detector.fit(
          x=x,
          y=y,
          batch_size=batch_size,
          nb_epochs=nb_epochs,
          drop_last=drop_last,
          scheduler=scheduler,
          **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 11 for calling the PyTorchObjectSeeker obj's underlying member method code with example usage: obj.fit(x, y, batch_size, nb_epochs, drop_last, scheduler) without return types