code: def create_settings(self):
      self.object_name = LabelSubscriber(
          "Select the input objects",
          "None",
          doc="Choose the name of the objects you want to convert to an image.",
      )
  
      self.image_name = ImageName(
          "Name the output image",
          "CellImage",
          doc="Enter the name of the resulting image.",
      )
  
      self.image_mode = Choice(
          "Select the color format",
          ["Color", "Binary (black & white)", "Grayscale", "uint16"],
          doc="""\
  Select which colors the resulting image should use. You have the
  following options:
  
  -  *Color:* Allows you to choose a colormap that will produce jumbled
     colors for your objects.
  -  *Binary (black & white):* All object pixels will be assigned 1 and
     all background pixels will be assigned 0, creating a binary image.
  -  *Grayscale:* Assigns all background pixels to 0 and assigns each object's pixels with a number 
     specific to that object. Object numbers can range from 1 to 255 (the maximum value that you can put
     in an 8-bit integer, use **uint16** if you expect more than 255 objects).
     This creates an image where objects in the top left corner of the image are
     very dark and the colors progress to white toward the bottom right corner of the image.
     Use **SaveImages** to save the resulting image as a .npy file or .tiff file if you want
     to process the label matrix image using another program or in a separate CellProfiler pipeline.
  -  *uint16:* Assigns all background pixels to 0 and assigns each object's pixels with a number 
     specific to that object. Object numbers can range from 1 to 65535 (the maximum value that you can put
     in a 16-bit integer). This creates an image where objects in the top left corner of the image are
     very dark and where the colors progress to white toward the bottom right corner of the image
     (though this can usually only be seen in a scientific image viewer since standard image viewers only
     handle 8-bit images). Use **SaveImages** to save the resulting image as a .npy file or
     **16-bit** (not 8-bit!) .tiff file if you want to process the label matrix image using another
     program or in a separate CellProfiler pipeline.
  
  You can choose *Color* with a *Gray* colormap to produce jumbled gray
  objects.
              """,
      )
  
      self.colormap = Colormap(
          "Select the colormap",
          doc="""\
  *(Used only if "Color" output image selected)*
  
  Choose the colormap to be used, which affects how the objects are
  colored. You can look up your default colormap under *File >
  Preferences*.
  """,
      )
  
file_path: CellProfiler/src/frontend/cellprofiler/modules/convertobjectstoimage.py
imports: import centrosome.cpmorphology
  import matplotlib.cm
  import numpy
  from cellprofiler_core.image import Image
  from cellprofiler_core.module import Module
  from cellprofiler_core.preferences import get_default_colormap
  from cellprofiler_core.setting.choice import Choice, Colormap
  from cellprofiler_core.setting.subscriber import LabelSubscriber
  from cellprofiler_core.setting.text import ImageName
label_desc: Use image node_id 1 for calling the ConvertObjectsToImage obj's underlying member method code with example usage: obj.create_settings() without return types