code: def _remove_confounds(conf_file):
      legal_confounds = pd.read_csv(
          conf_file, delimiter="\t", encoding="utf-8"
      )
      remove_columns = []
      for missing_kw in missing_keywords:
          remove_columns += [
              col_name
              for col_name in legal_confounds.columns
              if missing_kw in col_name
          ]
  
      aroma = [
          col_name
          for col_name in legal_confounds.columns
          if "aroma" in col_name
      ]
      missing_confounds = legal_confounds.drop(
          columns=missing_params + remove_columns + aroma
      )
      missing_confounds.to_csv(conf_file, sep="\t", index=False)
  
imports: import re
  import numpy
  import pandas
  import pytest
  from nibabel import Nifti1Image
  from scipy.stats import pearsonr
  from sklearn.preprocessing import scale
  from nilearn._utils.data_gen import create_fake_bids_dataset
  from nilearn._utils.fmriprep_confounds import to_camel_case
  from nilearn.conftest import _rng
  from nilearn.interfaces.bids import get_bids_files
  from nilearn.interfaces.fmriprep import load_confounds
  from nilearn.interfaces.fmriprep.load_confounds import _check_strategy, _load_single_confounds_file
  from nilearn.interfaces.fmriprep.tests._testing import create_tmp_filepath, get_legal_confound
  from nilearn.maskers import NiftiMasker
label_desc: Use image node_id 16 for calling a global function with example usage: _remove_confounds(conf_file) without return types