class_docstr: Dictionary-based wrapper of :py:class:`monai.apps.reconstruction.transforms.array.RandomKspacemask`.
  Other mask transforms can inherit from this class, for example:
  :py:class:`monai.apps.reconstruction.transforms.dictionary.EquispacedKspaceMaskd`.
  
  Args:
      keys: keys of the corresponding items to be transformed.
          See also: monai.transforms.MapTransform
      center_fractions: Fraction of low-frequency columns to be retained.
          If multiple values are provided, then one of these numbers is
          chosen uniformly each time.
      accelerations: Amount of under-sampling. This should have the
          same length as center_fractions. If multiple values are provided,
          then one of these is chosen uniformly each time.
      spatial_dims: Number of spatial dims (e.g., it's 2 for a 2D data; it's
          also 2 for pseudo-3D datasets like the fastMRI dataset).
          The last spatial dim is selected for sampling. For the fastMRI
          dataset, k-space has the form (...,num_slices,num_coils,H,W)
          and sampling is done along W. For a general 3D data with the
          shape (...,num_coils,H,W,D), sampling is done along D.
      is_complex: if True, then the last dimension will be reserved
          for real/imaginary parts.
      allow_missing_keys: don't raise exception if key is missing.
docstr: Args:
      data: is a dictionary containing (key,value) pairs from the
          loaded dataset
  
  Returns:
      the new data dictionary
docstr_tok: ["Args",":","data",":","is","a","dictionary","containing","(","key",",","value",")","pairs","from","the","loaded","dataset","Returns",":","the","new","data","dictionary"]
code: def __call__(
      self, data: Mapping[Hashable, NdarrayOrTensor]
  ) -> dict[Hashable, Tensor]:
      """
      Args:
          data: is a dictionary containing (key,value) pairs from the
              loaded dataset
  
      Returns:
          the new data dictionary
      """
      d = dict(data)
      for key in self.key_iterator(d):
          d[key + "_masked"], d[key + "_masked_ifft"] = self.masker(
              d[key]
          )
          d[FastMRIKeys.MASK] = self.masker.mask
  
      return d
  
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 3 for calling the RandomKspaceMaskd obj's underlying member method code with example usage: obj.__call__(data) and returns: d