code: def get_superpixel_segments(
      img: np.ndarray, segmentation_method: str
  ) -> np.ndarray:
      """
      Given an image, return segments or so-called 'super-pixels' segments i.e., an 2D mask with segment labels.
  
      Parameters
      ----------
      img: np.ndarray
              CxWxH image array.
      segmentation_method: string
          Indicates the segmentation method, i.e. "slic" or "felzenszwalb".
  
      Returns
      -------
      img: np.ndarray
          CxWxH segmented image array.
      """
  
      if img.ndim != 3:
          raise ValueError(
              "Make sure that x is 3 dimensional e.g., (3, 224, 224) to calculate super-pixels."
              f" shape: {img.shape}"
          )
      if segmentation_method not in ["slic", "felzenszwalb"]:
          raise ValueError(
              "'segmentation_method' must be either 'slic' or 'felzenszwalb'."
          )
  
      if segmentation_method == "slic":
          return slic(img, start_label=0)
      elif segmentation_method == "felzenszwalb":
          return felzenszwalb(
              img,
          )
  
imports: import copy
  import re
  from importlib import util
  from typing import Any, Dict, Optional, Sequence, Tuple, Union, List, TypeVar
  import numpy
  from skimage.segmentation import slic, felzenszwalb
  from quantus.helpers import asserts
  from quantus.helpers.model.model_interface import ModelInterface
label_desc: Use image node_id 1 for calling a global function with example usage: get_superpixel_segments(img, segmentation_method) and returns: slic, felzenszwalb