class_docstr: Use `skimage.segmentation.watershed` to get instance segmentation results from images.
  See: https://scikit-image.org/docs/stable/api/skimage.segmentation.html#skimage.segmentation.watershed.
  
  Args:
      connectivity: an array with the same number of dimensions as image whose non-zero elements indicate
          neighbors for connection. Following the scipy convention, default is a one-connected array of
          the dimension of the image.
      dtype: target data content type to convert, default is np.int64.
class_docstr_tok: ["Use","`","skimage.segmentation.watershed","`","to","get","instance","segmentation","results","from","images",".","See",":","https",":","\/\/scikit-image.org\/docs\/stable\/api\/skimage.segmentation.html","#","skimage.segmentation.watershed",".","Args",":","connectivity",":","an","array","with","the","same","number","of","dimensions","as","image","whose","non-zero","elements","indicate","neighbors","for","connection",".","Following","the","scipy","convention",",","default","is","a","one-connected","array","of","the","dimension","of","the","image",".","dtype",":","target","data","content","type","to","convert",",","default","is","np.int64","."]
code: def __init__(
      self, connectivity: int | None = 1, dtype: DtypeLike = np.int64
  ) -> None:
      self.connectivity = connectivity
      self.dtype = dtype
  
imports: from __future__ import annotations
  import warnings
  from typing import Callable, Sequence
  import numpy
  import torch
  from monai.config.type_definitions import DtypeLike, NdarrayOrTensor
  from monai.transforms import Activations, AsDiscrete, BoundingRect, FillHoles, GaussianSmooth, RemoveSmallObjects, SobelGradients
  from monai.transforms.transform import Transform
  from monai.transforms.utils_pytorch_numpy_unification import max, maximum, min, sum, unique
  from monai.utils import TransformBackends, convert_to_numpy, optional_import
  from monai.utils.misc import ensure_tuple_rep
  from monai.utils.type_conversion import convert_to_dst_type, convert_to_tensor
label_desc: Use image node_id 1 to create a new Watershed object from inherited base classes: Transform with example: obj = Watershed(connectivity, dtype)