class_docstr: Dictionary-based transform to convert MetaTensor to a dictionary.
  
  If input is `{"a": MetaTensor, "b": MetaTensor}`, then output will
  have the form `{"a": torch.Tensor, "a_meta_dict": dict, "a_transforms": list, "b": ...}`.
class_docstr_tok: ["Dictionary-based","transform","to","convert","MetaTensor","to","a","dictionary",".","If","input","is","`","{","``","a","''",":","MetaTensor",",","``","b","''",":","MetaTensor","}","`",",","then","output","will","have","the","form","`","{","``","a","''",":","torch.Tensor",",","``","a_meta_dict","''",":","dict",",","``","a_transforms","''",":","list",",","``","b","''",":","...","}","`","."]
code: def __call__(
      self, data: Mapping[Hashable, NdarrayOrTensor]
  ) -> dict[Hashable, NdarrayOrTensor]:
      d = dict(data)
      for key, t in self.key_iterator(d, self.as_tensor_output):
          im: MetaTensor = d[key]  # type: ignore
          d.update(
              im.as_dict(
                  key, output_type=torch.Tensor if t else np.ndarray
              )
          )
          self.push_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 FromMetaTensord obj's underlying member method code with example usage: obj.__call__(data) and returns: d