code: def watershed(
      input_image: numpy.ndarray,
      mask: numpy.ndarray = None,
      watershed_method: Literal[
          "distance", "intensity", "markers"
      ] = "distance",
      declump_method: Literal["shape", "intensity"] = "shape",
      seed_method: Literal["local", "regional"] = "local",
      intensity_image: numpy.ndarray = None,
      markers_image: numpy.ndarray = None,
      max_seeds: int = -1,
      downsample: int = 1,
      min_distance: int = 1,
      min_intensity: float = 0,
      footprint: int = 8,
      connectivity: int = 1,
      compactness: float = 0.0,
      exclude_border: bool = False,
      watershed_line: bool = False,
      gaussian_sigma: float = 0.0,
      structuring_element: Literal[
          "ball",
          "cube",
          "diamond",
          "disk",
          "octahedron",
          "square",
          "star",
      ] = "disk",
      structuring_element_size: int = 1,
      return_seeds: bool = False,
  ):
      y_data = library_watershed(
          input_image=input_image,
          mask=mask,
          watershed_method=watershed_method,
          declump_method=declump_method,
          seed_method=seed_method,
          intensity_image=intensity_image,
          markers_image=markers_image,
          max_seeds=max_seeds,
          downsample=downsample,
          min_distance=min_distance,
          min_intensity=min_intensity,
          footprint=footprint,
          connectivity=connectivity,
          compactness=compactness,
          exclude_border=exclude_border,
          watershed_line=watershed_line,
          gaussian_sigma=gaussian_sigma,
          structuring_element=structuring_element,
          structuring_element_size=structuring_element_size,
          return_seeds=return_seeds,
      )
      return y_data
  
imports: from typing import Literal
  import numpy
  from ..functions.object_processing import watershed
label_desc: Use image node_id 1 for calling a global function with example usage: watershed(input_image, mask, watershed_method, declump_method, seed_method, intensity_image, markers_image, max_seeds, downsample, min_distance, min_intensity, footprint, connectivity, compactness, exclude_border, watershed_line, gaussian_sigma, structuring_element, structuring_element_size, return_seeds) and returns: y_data