code: def save_object_image_crops(
      input_image,
      input_objects,
      save_dir,
      file_format="tiff8",
      nested_save=False,
      save_names={"input_filename": None, "input_objects_name": None},
      volumetric=False,
  ):
      """
      For a given input_objects array, save crops for each
      object of the provided input_image.
      """
      # Build save paths
      if nested_save:
          if (
              not save_names["input_filename"]
              and not save_names["input_objects_name"]
          ):
              raise ValueError(
                  "Must provide a save_names['input_filename'] or save_names['input_objects_name'] for nested save."
              )
          save_path = os.path.join(
              save_dir,
              save_names["input_filename"]
              if save_names["input_filename"]
              else save_names["input_objects_name"],
          )
      else:
          save_path = save_dir
  
      if not os.path.exists(save_path):
          os.makedirs(save_path, exist_ok=True)
  
      unique_labels = numpy.unique(input_objects)
  
      if unique_labels[0] == 0:
          unique_labels = unique_labels[1:]
  
      labels = input_objects
  
      if (
          len(input_image.shape) == len(input_objects.shape) + 1
          and not volumetric
      ):
          labels = numpy.repeat(
              labels[:, :, numpy.newaxis], input_image.shape[-1], axis=2
          )
  
      # Construct filename
      save_filename = f"{save_names['input_filename']+'_' if save_names['input_filename'] else ''}{save_names['input_objects_name']+'_' if save_names['input_objects_name'] else ''}"
  
      save_filenames = []
  
      for label in unique_labels:
          file_extension = "tiff" if "tiff" in file_format else "png"
  
          label_save_filename = os.path.join(
              save_path, save_filename + f"{label}.{file_extension}"
          )
          save_filenames.append(label_save_filename)
          mask_in = labels == label
          properties = skimage.measure.regionprops(
              mask_in.astype(int), intensity_image=input_image
          )
          mask = properties[0].intensity_image
  
          if file_format.casefold() == "png":
              skimage.io.imsave(
                  label_save_filename,
                  skimage.img_as_ubyte(mask),
                  check_contrast=False,
              )
          elif file_format.casefold() == "tiff8":
              skimage.io.imsave(
                  label_save_filename,
                  skimage.img_as_ubyte(mask),
                  compress=6,
                  check_contrast=False,
              )
          elif file_format.casefold() == "tiff16":
              skimage.io.imsave(
                  label_save_filename,
                  skimage.img_as_uint(mask),
                  compress=6,
                  check_contrast=False,
              )
          else:
              raise ValueError(
                  f"{file_format} not in 'png', 'tiff8', or 'tiff16'"
              )
  
      return save_filenames
  
imports: import os
  import numpy
  import skimage
label_desc: Use image node_id 1 for calling a global function with example usage: save_object_image_crops(input_image, input_objects, save_dir, file_format, nested_save, save_names, volumetric) and returns: save_filenames