class_docstr: This class extends monai.utils.ProbNMS and add the `resolution` option for
  Pathology.
class_docstr_tok: ["This","class","extends","monai.utils.ProbNMS","and","add","the","`","resolution","`","option","for","Pathology","."]
docstr: probs_map: the input probabilities map, it must have shape (H[, W, ...]).
  resolution_level: the level at which the probabilities map is made.
docstr_tok: ["probs_map",":","the","input","probabilities","map",",","it","must","have","shape","(","H","[",",","W",",","...","]",")",".","resolution_level",":","the","level","at","which","the","probabilities","map","is","made","."]
code: def __call__(
      self,
      probs_map: np.ndarray | torch.Tensor,
      resolution_level: int = 0,
  ) -> list[list]:
      """
      probs_map: the input probabilities map, it must have shape (H[, W, ...]).
      resolution_level: the level at which the probabilities map is made.
      """
      resolution = pow(2, resolution_level)
      org_outputs = ProbNMS.__call__(self, probs_map)
      outputs = []
      for org_output in org_outputs:
          prob = org_output[0]
          coord = np.asarray(org_output[1:])
          coord_wsi = ((coord + 0.5) * resolution).astype(int)
          outputs.append([prob] + list(coord_wsi))
      return outputs
  
file_path: MONAI/monai/apps/pathology/utils.py
imports: from __future__ import annotations
  from typing import Any
  import numpy
  import torch
  from monai.transforms.post.array import ProbNMS
  from monai.utils import optional_import
label_desc: Use image node_id 1 for calling the PathologyProbNMS obj's underlying member method code with example usage: obj.__call__(probs_map, resolution_level) and returns: outputs