class_docstr: Dictionary-based transform to convert a dictionary to MetaTensor.
  
  If input is `{"a": torch.Tensor, "a_meta_dict": dict, "b": ...}`, then output will
  have the form `{"a": MetaTensor, "b": MetaTensor}`.
class_docstr_tok: ["Dictionary-based","transform","to","convert","a","dictionary","to","MetaTensor",".","If","input","is","`","{","``","a","''",":","torch.Tensor",",","``","a_meta_dict","''",":","dict",",","``","b","''",":","...","}","`",",","then","output","will","have","the","form","`","{","``","a","''",":","MetaTensor",",","``","b","''",":","MetaTensor","}","`","."]
code: def inverse(
      self, data: Mapping[Hashable, NdarrayOrTensor]
  ) -> dict[Hashable, NdarrayOrTensor]:
      d = dict(data)
      for key in self.key_iterator(d):
          # check transform
          _ = self.get_most_recent_transform(d, key)
          # do the inverse
          im: MetaTensor = d[key]  # type: ignore
          d.update(im.as_dict(key))
          # Remove the applied transform
          self.pop_transform(d, key)
      return d
  
imports: from __future__ import annotations
  from collections.abc import Hashable, Mapping, Sequence
  import numpy
  import torch
  from monai.config.type_definitions import KeysCollection, NdarrayOrTensor
  from monai.data.meta_tensor import MetaTensor
  from monai.transforms.inverse import InvertibleTransform
  from monai.transforms.transform import MapTransform
  from monai.utils.enums import PostFix, TransformBackends
  from monai.utils.misc import ensure_tuple_rep
label_desc: Use image node_id 2 for calling the ToMetaTensord obj's underlying member method code with example usage: obj.inverse(data) and returns: d