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 __call__(self, data):
      d = dict(data)
      for key in self.key_iterator(d):
          seg = d[self.seg_pred_key]
          bbox = d[self.bbox_key]
          id = d[self.instance_id_key]
          instance_type, type_prob = self.converter(
              d[key], seg, bbox, id
          )
          key_to_add = f"{self.type_info_key}"
          if key_to_add in d:
              raise KeyError(
                  f"Type information with key {key_to_add} already exists."
              )
          d[key_to_add] = {
              "inst_type": instance_type,
              "type_prob": type_prob,
          }
      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 GenerateInstanceTyped obj's underlying member method code with example usage: obj.__call__(data) and returns: d