class_docstr: Dictionary-based wrapper of :py:class:`monai.apps.pathology.transforms.post.array.GenerateInstanceType`.
  Generate instance type and probability for each instance.
  
  Args:
      keys: keys of the corresponding items to be transformed.
      type_info_key: the output instance type and probability will be written to the value of
          `{type_info_key}`.
      bbox_key: keys of bounding box.
      seg_pred_key: keys of segmentation prediction map.
      instance_id_key: keys of instance id.
      allow_missing_keys: don't raise exception if key is missing.
code: def __init__(
      self,
      keys: KeysCollection,
      type_info_key: str = "type_info",
      bbox_key: str = "bbox",
      seg_pred_key: str = "seg",
      instance_id_key: str = "id",
      allow_missing_keys: bool = False,
  ) -> None:
      super().__init__(keys, allow_missing_keys)
      self.converter = GenerateInstanceType()
      self.type_info_key = type_info_key
      self.bbox_key = bbox_key
      self.seg_pred_key = seg_pred_key
      self.instance_id_key = instance_id_key
  
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 GenerateInstanceTyped object from inherited base classes: MapTransform with example: obj = GenerateInstanceTyped(keys, type_info_key, bbox_key, seg_pred_key, instance_id_key, allow_missing_keys)