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 __init__(
      self,
      keys: KeysCollection,
      mask_key: str = "mask",
      activation: str | Callable = "softmax",
      threshold: float | None = None,
      min_object_size: int = 10,
      dtype: DtypeLike = np.uint8,
      allow_missing_keys: bool = False,
  ) -> None:
      super().__init__(keys, allow_missing_keys)
      self.mask_key = mask_key
      self.transform = GenerateWatershedMask(
          activation=activation,
          threshold=threshold,
          min_object_size=min_object_size,
          dtype=dtype,
      )
  
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 1 to create a new GenerateWatershedMaskd object from inherited base classes: MapTransform with example: obj = GenerateWatershedMaskd(keys, mask_key, activation, threshold, min_object_size, dtype, allow_missing_keys)