code: def test_strategy_scrubbing(
      tmp_path, fmriprep_version, volumes_left, len_sample_mask
  ):
      """Check user specified input for scrubbing strategy."""
      file_nii, _ = create_tmp_filepath(
          tmp_path,
          image_type="regular",
          copy_confounds=True,
          copy_json=True,
          fmriprep_version=fmriprep_version,
      )
      confounds, sample_mask = load_confounds_strategy(
          file_nii, denoise_strategy="scrubbing", fd_threshold=0.15
      )
      # For "1.4.x"
      # out of 30 vols, should have 6 motion outliers from scrubbing,
      # and 2 vol removed by scrubbing strategy "full"
      assert len(sample_mask) == volumes_left
  
      # shape of confound regressors untouched
      assert confounds.shape[0] == 30
  
      # also load confounds with very liberal scrubbing thresholds
      # this should not produce an error
      confounds, sample_mask = load_confounds_strategy(
          file_nii,
          denoise_strategy="scrubbing",
          fd_threshold=1,
          std_dvars_threshold=5,
      )
      # only non-steady volumes removed
      assert len(sample_mask) == len_sample_mask
  
      # maker sure global signal works
      confounds, sample_mask = load_confounds_strategy(
          file_nii, denoise_strategy="scrubbing", global_signal="full"
      )
      for check in [
          "global_signal",
          "global_signal_derivative1",
          "global_signal_power2",
          "global_signal_derivative1_power2",
      ]:
          assert check in confounds.columns
  
file_path: nilearn/nilearn/interfaces/fmriprep/tests/test_load_confounds_strategy.py
imports: import re
  import pandas
  import pytest
  from nilearn.interfaces.fmriprep import load_confounds_strategy
  from nilearn.interfaces.fmriprep.load_confounds_strategy import preset_strategies
  from nilearn.interfaces.fmriprep.tests._testing import create_tmp_filepath
label_desc: Use image node_id 4 for calling a global function with example usage: test_strategy_scrubbing(tmp_path, fmriprep_version, volumes_left, len_sample_mask) without return types