old_contents: import numpy as np
  
  from chainercv.visualizations.vis_image import vis_image
  
  
  def vis_bbox(img, bbox, label=None, label_names=None, ax=None):
      """Visualize bounding boxes inside image.
  
      Example:
  
          >>> import chainercv
          >>> import matplotlib.pyplot as plot
          >>> dataset = chainercv.datasets.VOCDetectionDataset()
          >>> img, bbox, label = dataset[60]
          >>> chainercv.visualizations.vis_bbox(img, bbox, label, dataset.labels)
          >>> plot.show()
  
      Args:
          img (~numpy.ndarray): An array of shape :math:`(3, height, width)`.
              This is in BGR format and the range of its value is
              :math:`[0, 255]`.
          bbox (~numpy.ndarray): An array of shape :math:`(R, 5)`, where
              :math:`R` is the number of bounding boxes in the image. Elements
              are organized
              by :obj:`(x_min, y_min, x_max, y_max)` in the second axis.
          label (~numpy.ndarray): An integer array of shape :math:`(R,)`.
              The values correspond to id for label names stored in
              :obj:`label_names`. This is optional.
          label_names (iterable of strings): Name of labels ordered according
              to label_ids. If this is :obj:`None`, labels will be skipped.
          ax (matplotlib.axes.Axis): The visualization is displayed on this
              axis. If this is :obj:`None` (default), a new axis is created.
  
      Returns:
          ~matploblib.axes.Axes:
          Returns the Axes object with the plot for further tweaking.
  
      """
      from matplotlib import pyplot as plot
      # Returns newly instantiated matplotlib.axes.Axes object if ax is None
      ax = vis_image(img, ax=ax)
  
      for i, bbox_elem in enumerate(bbox):
          xy = (bbox_elem[0], bbox_elem[1])
          width = bbox_elem[2] - bbox_elem[0]
          height = bbox_elem[3] - bbox_elem[1]
          ax.add_patch(plot.Rectangle(
              xy, width, height, fill=False, edgecolor='red', linewidth=3))
          if label_names is not None:
              label_elem = label[i]
              ax.text(bbox_elem[0], bbox_elem[1],
                      label_names[label_elem.astype(np.int)],
                      style='italic',
                      bbox={'facecolor': 'white', 'alpha': 0.7, 'pad': 10})
      return ax
  
new_contents: import numpy as np
  
  from chainercv.visualizations.vis_image import vis_image
  
  
  def vis_bbox(img, bbox, label=None, label_names=None, ax=None):
      """Visualize bounding boxes inside image.
  
      Example:
  
          >>> import chainercv
          >>> import matplotlib.pyplot as plot
          >>> dataset = chainercv.datasets.VOCDetectionDataset()
          >>> img, bbox, label = dataset[60]
          >>> chainercv.visualizations.vis_bbox(img, bbox, label, dataset.labels)
          >>> plot.show()
  
      Args:
          img (~numpy.ndarray): An array of shape :math:`(3, height, width)`.
              This is in BGR format and the range of its value is
              :math:`[0, 255]`.
          bbox (~numpy.ndarray): An array of shape :math:`(R, 4)`, where
              :math:`R` is the number of bounding boxes in the image. Elements
              are organized
              by :obj:`(x_min, y_min, x_max, y_max)` in the second axis.
          label (~numpy.ndarray): An integer array of shape :math:`(R,)`.
              The values correspond to id for label names stored in
              :obj:`label_names`. This is optional.
          label_names (iterable of strings): Name of labels ordered according
              to label_ids. If this is :obj:`None`, labels will be skipped.
          ax (matplotlib.axes.Axis): The visualization is displayed on this
              axis. If this is :obj:`None` (default), a new axis is created.
  
      Returns:
          ~matploblib.axes.Axes:
          Returns the Axes object with the plot for further tweaking.
  
      """
      from matplotlib import pyplot as plot
      # Returns newly instantiated matplotlib.axes.Axes object if ax is None
      ax = vis_image(img, ax=ax)
  
      for i, bbox_elem in enumerate(bbox):
          xy = (bbox_elem[0], bbox_elem[1])
          width = bbox_elem[2] - bbox_elem[0]
          height = bbox_elem[3] - bbox_elem[1]
          ax.add_patch(plot.Rectangle(
              xy, width, height, fill=False, edgecolor='red', linewidth=3))
          if label_names is not None:
              label_elem = label[i]
              ax.text(bbox_elem[0], bbox_elem[1],
                      label_names[label_elem.astype(np.int)],
                      style='italic',
                      bbox={'facecolor': 'white', 'alpha': 0.7, 'pad': 10})
      return ax
  
subject: fix the shape of bbox