code: def get_baseline_dict(
      arr: np.ndarray, patch: Optional[np.ndarray] = None, **kwargs
  ) -> dict:
      """
      Make a dictionary of baseline approaches depending on the input x (or patch of input).
  
      Parameters
      ----------
      arr: np.ndarray
          CxWxH image array used to calculate baseline values, i.e. for "mean", "black" and "white" methods.
      patch: np.ndarray, optional
          CxWxH patch array to calculate baseline values, necessary for "neighbourhood_mean" and
          "neighbourhood_random_min_max" methods.
      kwargs: optional
              Keyword arguments..
  
      Returns
      -------
          fill_dict: dict
              Maps all available baseline methods to baseline values.
      """
      fill_dict = {
          "mean": float(arr.mean()),
          "uniform": np.random.uniform(
              low=kwargs.get("uniform_low", 0.0),
              high=kwargs.get("uniform_high", 1.0),
              size=kwargs["return_shape"],
          ),
          "black": float(arr.min()),
          "white": float(arr.max()),
      }
      if patch is not None:
          fill_dict["neighbourhood_mean"] = (float(patch.mean()),)
          fill_dict["neighbourhood_random_min_max"] = float(
              np.random.uniform(low=patch.min(), high=patch.max())
          )
      return fill_dict
  
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 3 for calling a global function with example usage: get_baseline_dict(arr, patch) and returns: fill_dict