class_docstr: Dictionary-based wrapper for :py:class:`monai.apps.pathology.transforms.post.array.HoVerNetNuclearTypePostProcessing`.
  It updates the input instance info dictionary with information about types of the nuclei (value and probability).
  Also if requested (`return_type_map=True`), it generates a pixel-level type map.
  
  Args:
      type_prediction_key: the key for HoVerNet NC (type prediction) branch. Defaults to `HoVerNetBranch.NC`.
      instance_info_key: the key where instance information (contour, bounding boxes, and centroids) is stored.
          Defaults to `"instance_info"`.
      instance_map_key: the key where instance map is stored. Defaults to `"instance_map"`.
      type_map_key: the output key where type map is written. Defaults to `"type_map"`.
      device: target device to put the output Tensor data.
code: def __init__(
      self,
      type_prediction_key: str = HoVerNetBranch.NC.value,
      instance_info_key: str = "instance_info",
      instance_map_key: str = "instance_map",
      type_map_key: str = "type_map",
      activation: str | Callable = "softmax",
      threshold: float | None = None,
      return_type_map: bool = True,
      device: str | torch.device | None = None,
  ) -> None:
      super().__init__()
      self.type_post_process = HoVerNetNuclearTypePostProcessing(
          activation=activation,
          threshold=threshold,
          return_type_map=return_type_map,
          device=device,
      )
      self.type_prediction_key = type_prediction_key
      self.instance_info_key = instance_info_key
      self.instance_map_key = instance_map_key
      self.type_map_key = type_map_key
      self.return_type_map = return_type_map
  
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 HoVerNetNuclearTypePostProcessingd object from inherited base classes: Transform with example: obj = HoVerNetNuclearTypePostProcessingd(type_prediction_key, instance_info_key, instance_map_key, type_map_key, activation, threshold, return_type_map, device)