class_docstr: Dictionary-based wrapper of
  :py:class:`monai.transforms.NormalizeIntensity`.
  This is similar to :py:class:`monai.transforms.NormalizeIntensityd`
  and can normalize non-zero values or the entire image. The difference
  is that this transform does normalization according to a reference image.
  
  Args:
      keys: keys of the corresponding items to be transformed.
          See also: monai.transforms.MapTransform
      ref_key: key of the item to be used to normalize items of "keys"
      subtrahend: the amount to subtract by (usually the mean)
      divisor: the amount to divide by (usually the standard deviation)
      nonzero: whether only normalize non-zero values.
      channel_wise: if True, calculate on each channel separately,
          otherwise, calculate on the entire image directly. default
          to False.
      dtype: output data type, if None, same as input image. defaults
          to float32.
      allow_missing_keys: don't raise exception if key is missing.
  
  Example:
      In an image reconstruction task, let keys=["image", "target"] and ref_key=["image"].
      Also, let data be the data dictionary. Then, ReferenceBasedNormalizeIntensityd
      normalizes data["target"] and data["image"] based on the mean-std of data["image"] by
      calling :py:class:`monai.transforms.NormalizeIntensity`.
code: def __init__(
      self,
      keys: KeysCollection,
      ref_key: str,
      subtrahend: NdarrayOrTensor | None = None,
      divisor: NdarrayOrTensor | None = None,
      nonzero: bool = False,
      channel_wise: bool = False,
      dtype: DtypeLike = np.float32,
      allow_missing_keys: bool = False,
  ) -> None:
      super().__init__(keys, allow_missing_keys)
      self.default_normalizer = NormalizeIntensity(
          subtrahend, divisor, nonzero, channel_wise, dtype
      )
      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 ReferenceBasedNormalizeIntensityd object from inherited base classes: MapTransform with example: obj = ReferenceBasedNormalizeIntensityd(keys, ref_key, subtrahend, divisor, nonzero, channel_wise, dtype, allow_missing_keys)