docstr: Display the overlaid images
  
  workspace - the workspace being run, with display_data holding:
      image_info - a list of lists:
           input image name of image being aligned
           input image data
           output image name of image being aligned
           output image data
           x offset
           y offset
docstr_tok: ["Display","the","overlaid","images","workspace","-","the","workspace","being","run",",","with","display_data","holding",":","image_info","-","a","list","of","lists",":","input","image","name","of","image","being","aligned","input","image","data","output","image","name","of","image","being","aligned","output","image","data","x","offset","y","offset"]
code: def display(self, workspace, figure):
      """Display the overlaid images
  
      workspace - the workspace being run, with display_data holding:
          image_info - a list of lists:
               input image name of image being aligned
               input image data
               output image name of image being aligned
               output image data
               x offset
               y offset
      """
      image_info = workspace.display_data.image_info
      first_input_name = self.first_input_image.value
      first_output_name = self.first_output_image.value
      figure.set_subplots((2, len(image_info) - 1))
  
      first_input_pixels = image_info[0][1]
      first_output_pixels = image_info[0][3]
      for (
          j,
          (
              input_name,
              input_pixels,
              output_name,
              output_pixels,
              off_x,
              off_y,
              shape,
          ),
      ) in enumerate(image_info[1:]):
          unaligned_title = "Unaligned images: %s and %s" % (
              first_input_name,
              input_name,
          )
          #
          # Make them grayscale if needed
          #
          first_pixels, other_pixels = [
              img if img.ndim == 2 else np.mean(img, 2)
              for img in (first_input_pixels, input_pixels)
          ]
          max_shape = np.maximum(first_pixels.shape, other_pixels.shape)
          img = np.zeros((max_shape[0], max_shape[1], 3))
          img[
              : first_pixels.shape[0], : first_pixels.shape[1], 0
          ] = first_pixels
          img[
              : other_pixels.shape[0], : other_pixels.shape[1], 1
          ] = other_pixels
          figure.subplot_imshow(
              0, j, img, unaligned_title, sharexy=figure.subplot(0, 0)
          )
  
          aligned_title = (
              "Aligned images: %s and %s\nX offset: %d, Y offset: %d"
              % (
                  first_output_name,
                  output_name,
                  -off_x,
                  -off_y,
              )
          )
          first_pixels, other_pixels = [
              img if img.ndim == 2 else np.mean(img, 2)
              for img in (first_output_pixels, output_pixels)
          ]
          max_shape = np.maximum(first_pixels.shape, other_pixels.shape)
          img = np.zeros((max_shape[0], max_shape[1], 3))
          img[
              : first_pixels.shape[0], : first_pixels.shape[1], 0
          ] = first_pixels
          img[
              : other_pixels.shape[0], : other_pixels.shape[1], 1
          ] = other_pixels
          figure.subplot_imshow(
              1, j, img, aligned_title, sharexy=figure.subplot(0, 0)
          )
  
imports: import numpy
  import scipy.ndimage
  import scipy.sparse
  from centrosome.filter import stretch
  from scipy.fftpack import fft2, ifft2
  from ..constants.measurement import COLTYPE_INTEGER
  from ..image import Image
  from ..module import Module
  from ..setting import Divider, SettingsGroup
  from ..setting.choice import Choice
  from ..setting.do_something import DoSomething, RemoveSettingButton
  from ..setting.subscriber import ImageSubscriber
  from ..setting.text import ImageName
label_desc: Use image node_id 7 for calling the Align obj's underlying member method code with example usage: obj.display(workspace, figure) without return types