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": ...}`.
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docstr: Args:
      keys: keys of the corresponding items to be transformed.
          See also: :py:class:`monai.transforms.compose.MapTransform`
      data_type: target data type to convert, should be "tensor" or "numpy".
      allow_missing_keys: don't raise exception if key is missing.
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code: def __init__(
      self,
      keys: KeysCollection,
      data_type: Sequence[str] | str = "tensor",
      allow_missing_keys: bool = False,
  ):
      """
      Args:
          keys: keys of the corresponding items to be transformed.
              See also: :py:class:`monai.transforms.compose.MapTransform`
          data_type: target data type to convert, should be "tensor" or "numpy".
          allow_missing_keys: don't raise exception if key is missing.
      """
      super().__init__(keys, allow_missing_keys)
      self.as_tensor_output = tuple(
          d == "tensor"
          for d in ensure_tuple_rep(data_type, len(self.keys))
      )
  
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 1 to create a new FromMetaTensord object from inherited base classes: MapTransform, InvertibleTransform with example: obj = FromMetaTensord(keys, data_type, allow_missing_keys)