class_docstr: Class encapsulating a batch during the augmentation process.
  
  Data within the batch is already verified and normalized, similar to
  :class:`Batch`. Data within the batch may be changed in-place. No initial
  copy is needed.
  
  Added in 0.4.0.
  
  Parameters
  ----------
  images : None or (N,H,W,C) ndarray or list of (H,W,C) ndarray
      The images to augment.
  
  heatmaps : None or list of imgaug.augmentables.heatmaps.HeatmapsOnImage
      The heatmaps to augment.
  
  segmentation_maps : None or list of imgaug.augmentables.segmaps.SegmentationMapsOnImage
      The segmentation maps to augment.
  
  keypoints : None or list of imgaug.augmentables.kps.KeypointOnImage
      The keypoints to augment.
  
  bounding_boxes : None or list of imgaug.augmentables.bbs.BoundingBoxesOnImage
      The bounding boxes to augment.
  
  polygons : None or list of imgaug.augmentables.polys.PolygonsOnImage
      The polygons to augment.
  
  line_strings : None or list of imgaug.augmentables.lines.LineStringsOnImage
      The line strings to augment.
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docstr: Get the shape of each row within this batch.
  
  Each row denotes the data of different types (e.g. image array,
  polygons) corresponding to a single example in the batch.
  
  This method assumes that all ``.shape`` attributes contain the same
  shape and that it is identical to the image's shape.
  It also assumes that there are no columns containing only ``None`` s.
  
  Added in 0.4.0.
  
  Returns
  -------
  list of tuple of int
      The shapes of each row.
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code: def get_rowwise_shapes(self):
      """Get the shape of each row within this batch.
  
      Each row denotes the data of different types (e.g. image array,
      polygons) corresponding to a single example in the batch.
  
      This method assumes that all ``.shape`` attributes contain the same
      shape and that it is identical to the image's shape.
      It also assumes that there are no columns containing only ``None`` s.
  
      Added in 0.4.0.
  
      Returns
      -------
      list of tuple of int
          The shapes of each row.
  
      """
      nb_rows = self.nb_rows
      columns = self.columns
      shapes = [None] * nb_rows
      found = np.zeros((nb_rows,), dtype=bool)
      for column in columns:
          if column.name == "images" and ia.is_np_array(column.value):
              shapes = [column.value.shape[1:]] * nb_rows
          else:
              for i, item in enumerate(column.value):
                  if item is not None:
                      shapes[i] = item.shape
                      found[i] = True
          if np.all(found):
              return shapes
      return shapes
  
imports: from __future__ import print_function, division, absolute_import
  import collections
  import numpy
  from ..None import imgaug
  from .None import normalization
  from .None import utils
label_desc: Use image node_id 6 for calling the _BatchInAugmentation obj's underlying member method code with example usage: obj.get_rowwise_shapes() and returns: shapes, shapes