class_docstr: Dictionary-based wrapper of :py:class:`monai.apps.pathology.transforms.array.GenerateWatershedMask`.
  
  Args:
      keys: keys of the corresponding items to be transformed.
      mask_key: the mask will be written to the value of `{mask_key}`.
      activation: the activation layer to be applied on nuclear type branch. It can be "softmax" or "sigmoid" string,
          or any callable. Defaults to "softmax".
      threshold: if not None, threshold the float values to int number 0 or 1 with specified threshold.
      min_object_size: objects smaller than this size are removed. Defaults to 10.
      dtype: target data content type to convert, default is np.uint8.
      allow_missing_keys: don't raise exception if key is missing.
code: def __call__(
      self, data: Mapping[Hashable, NdarrayOrTensor]
  ) -> dict[Hashable, NdarrayOrTensor]:
      d = dict(data)
      for key in self.key_iterator(d):
          mask = self.transform(d[key])
          if self.mask_key in d:
              raise KeyError(
                  f"Mask with key {self.mask_key} already exists."
              )
          d[self.mask_key] = mask
      return d
  
imports: from __future__ import annotations
  from collections.abc import Callable, Hashable, Mapping
  import numpy
  import torch
  from monai.apps.pathology.transforms.post.array import GenerateDistanceMap, GenerateInstanceBorder, GenerateInstanceCentroid, GenerateInstanceContour, GenerateInstanceType, GenerateSuccinctContour, GenerateWatershedMarkers, GenerateWatershedMask, HoVerNetInstanceMapPostProcessing, HoVerNetNuclearTypePostProcessing, Watershed
  from monai.config.type_definitions import DtypeLike, KeysCollection, NdarrayOrTensor
  from monai.transforms.transform import MapTransform, Transform
  from monai.utils import optional_import
  from monai.utils.enums import HoVerNetBranch
label_desc: Use image node_id 2 for calling the GenerateWatershedMaskd obj's underlying member method code with example usage: obj.__call__(data) and returns: d