class_docstr: Load fastMRI files with '.h5' suffix. fastMRI files, when loaded with "h5py",
  are HDF5 dictionary-like datasets. The keys are:
  
  - kspace: contains the fully-sampled kspace
  - reconstruction_rss: contains the root sum of squares of ifft of kspace. This
      is the ground-truth image.
  
  It also has several attributes with the following keys:
  
  - acquisition (str): acquisition mode of the data (e.g., AXT2 denotes T2 brain MRI scans)
  - max (float): dynamic range of the data
  - norm (float): norm of the kspace
  - patient_id (str): the patient's id whose measurements were recorded
docstr: Get all the metadata of the loaded dict and return the meta dict.
  
  Args:
      dat: a dictionary object loaded from an h5 file.
docstr_tok: ["Get","all","the","metadata","of","the","loaded","dict","and","return","the","meta","dict",".","Args",":","dat",":","a","dictionary","object","loaded","from","an","h5","file","."]
code: def _get_meta_dict(self, dat: dict) -> dict:
      """
      Get all the metadata of the loaded dict and return the meta dict.
  
      Args:
          dat: a dictionary object loaded from an h5 file.
      """
      return {
          k.value: dat[k.value] for k in FastMRIKeys if k.value in dat
      }
  
file_path: MONAI/monai/apps/reconstruction/fastmri_reader.py
imports: from __future__ import annotations
  import os
  from collections.abc import Sequence
  import numpy
  from numpy import ndarray
  from monai.config import PathLike
  from monai.data.image_reader import ImageReader
  from monai.data.utils import is_supported_format
  from monai.utils import FastMRIKeys, optional_import, require_pkg
label_desc: Use image node_id 4 for calling the FastMRIReader obj's underlying member method code with example usage: obj._get_meta_dict(dat) and returns: unknown