class_docstr: Dictionary-based wrapper of :py:class:`monai.apps.pathology.transforms.array.GenerateDistanceMap`.
  
  Args:
      mask_key: the input key where the watershed mask is stored. Defaults to `"mask"`.
      border_key: the input key where instance border map is stored. Defaults to `"border"`.
      dist_map_key: the output key where distance map is written. Defaults to `"dist_map"`.
      smooth_fn: smoothing function for distance map, which can be any callable object.
          If not provided :py:class:`monai.transforms.GaussianSmooth()` is used.
      dtype: target data content type to convert, default is np.float32.
code: def __init__(
      self,
      mask_key: str = "mask",
      border_key: str = "border",
      dist_map_key: str = "dist_map",
      smooth_fn: Callable | None = None,
      dtype: DtypeLike = np.float32,
  ) -> None:
      self.mask_key = mask_key
      self.border_key = border_key
      self.dist_map_key = dist_map_key
      self.transform = GenerateDistanceMap(
          smooth_fn=smooth_fn, 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 GenerateDistanceMapd object from inherited base classes: Transform with example: obj = GenerateDistanceMapd(mask_key, border_key, dist_map_key, smooth_fn, dtype)