class_docstr: Assign class value from the labelmap.  This converts multi-dimension tensor to single scalar tensor.
  
  Args:
      offset: offset value to be added to the mask value to determine the final class
class_docstr_tok: ["Assign","class","value","from","the","labelmap",".","This","converts","multi-dimension","tensor","to","single","scalar","tensor",".","Args",":","offset",":","offset","value","to","be","added","to","the","mask","value","to","determine","the","final","class"]
code: def __call__(self, data):
      d = dict(data)
      for key in self.keys:
          label = (
              d[key]
              if isinstance(d[key], torch.Tensor)
              else torch.from_numpy(d[key])
          )
          mask_value = int(torch.max(label))
          d[key] = mask_value + self.offset
      return d
  
imports: from __future__ import annotations
  import math
  from typing import Any
  import numpy
  import torch
  from monai.config import KeysCollection, NdarrayOrTensor
  from monai.networks.layers import GaussianFilter
  from monai.transforms import MapTransform, Randomizable, SpatialPad
  from monai.utils import StrEnum, convert_to_numpy, optional_import
label_desc: Use image node_id 2 for calling the SetLabelClassd obj's underlying member method code with example usage: obj.__call__(data) and returns: d