class_docstr: Base class of Faster R-CNN with FPN.
  
  This is a base class of Faster R-CNN with FPN.
  
  Args:
      extractor (Link): A link that extracts feature maps.
          This link must have :obj:`scales`, :obj:`mean` and
          :meth:`forward`.
      rpn (Link): A link that has the same interface as
          :class:`~chainercv.links.model.fpn.RPN`.
          Please refer to the documentation found there.
      bbox_head (Link): A link that has the same interface as
          :class:`~chainercv.links.model.fpn.BboxHead`.
          Please refer to the documentation found there.
      mask_head (Link): A link that has the same interface as
          :class:`~chainercv.links.model.fpn.MaskHead`.
          Please refer to the documentation found there.
      return_values (list of strings): Determines the values
          returned by :meth:`predict`.
      min_size (int): A preprocessing paramter for :meth:`prepare`. Please
          refer to a docstring found for :meth:`prepare`.
      max_size (int): A preprocessing paramter for :meth:`prepare`. Note
          that the result of :meth:`prepare` can exceed this size due to
          alignment with stride.
  
  Parameters:
      nms_thresh (float): The threshold value
          for :func:`~chainercv.utils.non_maximum_suppression`.
          The default value is :obj:`0.45`.
          This value can be changed directly or by using :meth:`use_preset`.
      score_thresh (float): The threshold value for confidence score.
          If a bounding box whose confidence score is lower than this value,
          the bounding box will be suppressed.
          The default value is :obj:`0.6`.
          This value can be changed directly or by using :meth:`use_preset`.
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docstr: Preprocess images.
  
  Args:
      imgs (iterable of numpy.ndarray): Arrays holding images.
          All images are in CHW and RGB format
          and the range of their value is :math:`[0, 255]`.
  
  Returns:
      Two arrays: preprocessed images and             scales that were caluclated in prepocessing.
docstr_tok: ["Preprocess","images",".","Args",":","imgs","(","iterable","of","numpy.ndarray",")",":","Arrays","holding","images",".","All","images","are","in","CHW","and","RGB","format","and","the","range","of","their","value","is",":","math",":","`","[","0",",","255","]","`",".","Returns",":","Two","arrays",":","preprocessed","images","and","scales","that","were","caluclated","in","prepocessing","."]
code: def prepare(self, imgs):
      """Preprocess images.
  
          Args:
              imgs (iterable of numpy.ndarray): Arrays holding images.
                  All images are in CHW and RGB format
                  and the range of their value is :math:`[0, 255]`.
  
          Returns:
              Two arrays: preprocessed images and \
              scales that were caluclated in prepocessing.
  
          """
      scales = []
      resized_imgs = []
      for img in imgs:
          img, scale = scale_img(img, self._min_size, self._max_size)
          img -= self.extractor.mean
          scales.append(scale)
          resized_imgs.append(img)
      pad_size = np.array([im.shape[1:] for im in resized_imgs]).max(
          axis=0
      )
      pad_size = (np.ceil(pad_size / self.stride) * self.stride).astype(
          int
      )
      x = np.zeros(
          (len(imgs), 3, pad_size[0], pad_size[1]), dtype=np.float32
      )
      for i, im in enumerate(resized_imgs):
          _, H, W = im.shape
          x[i, :, :H, :W] = im
      x = self.xp.array(x)
  
      return x, scales
  
file_path: chainercv/chainercv/links/model/fpn/faster_rcnn.py
imports: from __future__ import division
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.functions
  from chainercv.links.model.fpn.misc import scale_img
label_desc: Use image node_id 5 for calling the FasterRCNN obj's underlying member method code with example usage: obj.prepare(imgs) and returns: x,  scales