old_contents: import numpy as np
  
  
  def vis_label(label, alpha=1, label_names=None, ax=None):
      """Visualize label of semantic segmentation.
  
      Example:
  
          >>> from chainercv.datasets import VOCSemanticSegmentationDataset
          >>> from chainercv.datasets \
          ...     import voc_semantic_segmentation_label_names
          >>> from chainercv.visualizations import vis_image
          >>> from chainercv.visualizations import vis_label
          >>> import matplotlib.pyplot as plot
          >>> dataset = VOCSemanticSegmentationDataset()
          >>> img, label = dataset[60]
          >>> ax = vis_image(img)
          >>> vis_label(label, alpha=0.75,
          ...           label_names=voc_semantic_segmentation_label_names, ax=ax)
          >>> plot.show()
  
      Args:
          label (~numpy.ndarray): An integer array of shape
              :math:`(height, width)`.
              The values correspond to id for label names stored in
              :obj:`label_names`.
          alpha (float): The value which determines transparency of the figure.
              The range of this value is :math:`[0, 1]`. If this
              value is :obj:`0`, the figure will be completely transparent.
              The default value is :obj:`1`. This option is useful for
              overlaying labels on the source images.
          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
  
      if label_names is None:
          n_class = label.max()
      else:
          n_class = len(label_names) + 1
  
      cmap = plot.get_cmap()
  
      img = cmap(label / (n_class - 1))
      # If label is invalid, alpha = 0.
      # Otherwise, alpha = alpha
      img[:, :, 3] = np.where(label >= 0, alpha, 0)
  
      if ax is None:
          fig = plot.figure()
          ax = fig.add_subplot(1, 1, 1)
  
      ax.imshow(img)
  
      return ax
  
new_contents: import numpy as np
  
  
  def vis_label(label, label_names=None, alpha=1, ax=None):
      """Visualize label of semantic segmentation.
  
      Example:
  
          >>> from chainercv.datasets import VOCSemanticSegmentationDataset
          >>> from chainercv.datasets \
          ...     import voc_semantic_segmentation_label_names
          >>> from chainercv.visualizations import vis_image
          >>> from chainercv.visualizations import vis_label
          >>> import matplotlib.pyplot as plot
          >>> dataset = VOCSemanticSegmentationDataset()
          >>> img, label = dataset[60]
          >>> ax = vis_image(img)
          >>> vis_label(
          ...     label, label_names=voc_semantic_segmentation_label_names,
          ...     alpha=0.75, ax=ax)
          >>> plot.show()
  
      Args:
          label (~numpy.ndarray): An integer array of shape
              :math:`(height, width)`.
              The values correspond to id for label names stored in
              :obj:`label_names`.
          label_names (iterable of strings): Name of labels ordered according
              to label ids. If this is :obj:`None`, labels will be skipped.
          alpha (float): The value which determines transparency of the figure.
              The range of this value is :math:`[0, 1]`. If this
              value is :obj:`0`, the figure will be completely transparent.
              The default value is :obj:`1`. This option is useful for
              overlaying labels on the source images.
          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
  
      if label_names is None:
          n_class = label.max()
      else:
          n_class = len(label_names) + 1
  
      cmap = plot.get_cmap()
  
      img = cmap(label / (n_class - 1))
      # If label is invalid, alpha = 0.
      # Otherwise, alpha = alpha
      img[:, :, 3] = np.where(label >= 0, alpha, 0)
  
      if ax is None:
          fig = plot.figure()
          ax = fig.add_subplot(1, 1, 1)
  
      ax.imshow(img)
  
      return ax
  
subject: change the order of arguments