code: def _load_ica_aroma(confounds_raw, ica_aroma):
      """Load the ICA-AROMA regressors.
  
      Parameters
      ----------
      confounds_raw : pandas.DataFrame
          DataFrame of confounds.
  
      ica_aroma : str
          ICA-AROMA strategy to use. Options are "full", "basic".
  
      Returns
      -------
      pandas.DataFrame
          DataFrame of ICA-AROMA regressors.
          When ica_aroma is "full", return an empty DataFrame as the
          ICA-AROMA regressors have been handled in the preprocessed bold
          image.
  
      Raises
      ------
      ValueError
          When ica_aroma is not "full" or "basic".
      """
      if ica_aroma == "full":
          return pd.DataFrame()
      elif ica_aroma == "basic":
          ica_aroma_params = find_confounds(confounds_raw, ["aroma"])
          if not ica_aroma_params:
              raise MissingConfound(keywords=["ica_aroma"])
          return confounds_raw[ica_aroma_params]
      else:
          raise ValueError(
              "Please select an option when using ICA-AROMA strategy."
              f"Current input: {ica_aroma}"
          )
  
file_path: nilearn/nilearn/interfaces/fmriprep/load_confounds_components.py
imports: import numpy
  import pandas
  from .load_confounds_compcor import find_compcor
  from .load_confounds_scrub import optimize_scrub
  from .load_confounds_utils import MissingConfound, add_suffix, check_params, find_confounds
label_desc: Use image node_id 6 for calling a global function with example usage: _load_ica_aroma(confounds_raw, ica_aroma) and returns: pd, confounds_raw