class_docstr: Dictionary-based wrapper of :py:class:`monai.transforms.SpatialCrop`.
  This is similar to :py:class:`monai.transforms.SpatialCropd` which is a
  general purpose cropper to produce sub-volume region of interest (ROI).
  Their difference is that this transform does cropping according to a reference image.
  
  If a dimension of the expected ROI size is larger than the input image size, will not crop that dimension.
  
  Args:
      keys: keys of the corresponding items to be transformed.
          See also: :py:class:`monai.transforms.compose.MapTransform`
      ref_key: key of the item to be used to crop items of "keys"
      allow_missing_keys: don't raise exception if key is missing.
  
  Example:
      In an image reconstruction task, let keys=["image"] and ref_key=["target"].
      Also, let data be the data dictionary. Then, ReferenceBasedSpatialCropd
      center-crops data["image"] based on the spatial size of data["target"] by
      calling :py:class:`monai.transforms.SpatialCrop`.
code: def __init__(
      self,
      keys: KeysCollection,
      ref_key: str,
      allow_missing_keys: bool = False,
  ) -> None:
      MapTransform.__init__(self, keys, allow_missing_keys)
      self.ref_key = ref_key
  
imports: from __future__ import annotations
  from collections.abc import Hashable, Mapping, Sequence
  import numpy
  from numpy import ndarray
  from torch import Tensor
  from monai.apps.reconstruction.transforms.array import EquispacedKspaceMask, RandomKspaceMask
  from monai.config import DtypeLike, KeysCollection
  from monai.config.type_definitions import NdarrayOrTensor
  from monai.transforms import InvertibleTransform
  from monai.transforms.croppad.array import SpatialCrop
  from monai.transforms.intensity.array import NormalizeIntensity
  from monai.transforms.transform import MapTransform, RandomizableTransform
  from monai.utils import FastMRIKeys
  from monai.utils.type_conversion import convert_to_tensor
label_desc: Use image node_id 1 to create a new ReferenceBasedSpatialCropd object from inherited base classes: MapTransform, InvertibleTransform with example: obj = ReferenceBasedSpatialCropd(keys, ref_key, allow_missing_keys)