code: def load_confounds_file_as_dataframe(confounds_raw_path):
      """Load raw confounds as a pandas DataFrame.
  
      Meanwhile detect if the fMRIPrep version is supported.
  
      Parameters
      ----------
      confounds_raw_path : str
          Path to the confounds file.
  
      Returns
      -------
      confounds_raw : pandas.DataFrame
          Raw confounds loaded from the confounds file.
      """
      confounds_raw = pd.read_csv(
          confounds_raw_path, delimiter="\t", encoding="utf-8"
      )
  
      # check if the version of fMRIprep (>=1.2.0) is supported based on
      # header format. 1.0.x and 1.1.x series uses camel case
      if any(
          is_camel_case(col_name) for col_name in confounds_raw.columns
      ):
          raise ValueError(
              "The confound file contains header in camel case. "
              "This is likely the output from 1.0.x and 1.1.x series. "
              "We only support fmriprep outputs >= 1.2.0."
              f"{confounds_raw.columns}"
          )
  
      # even old version with no header will have the first row as header
      try:
          too_old = float(confounds_raw.columns[0])
      except ValueError:
          too_old = False
  
      if too_old:
          bad_file = pd.read_csv(
              confounds_raw_path,
              delimiter="\t",
              encoding="utf-8",
              header=None,
          )
          raise ValueError(
              "The confound file contains no header."
              "Is this an old version fMRIprep output?"
              f"{bad_file.head()}"
          )
      return confounds_raw
  
imports: import itertools
  import json
  import os
  import re
  import numpy
  import pandas
  from sklearn.preprocessing import scale
  from nilearn._utils.fmriprep_confounds import flag_single_gifti, is_camel_case
  from nilearn.interfaces.bids import parse_bids_filename
  from .load_confounds_scrub import extract_outlier_regressors
label_desc: Use image node_id 10 for calling a global function with example usage: load_confounds_file_as_dataframe(confounds_raw_path) and returns: confounds_raw