code: def _threshold(data, threshold, vmin, vmax):
      """Thresholds the data."""
      # If no thresholding and nans, filter them out
      if threshold is None:
          mask = np.logical_not(np.isnan(data))
      else:
          mask = np.abs(data) >= threshold
          if vmin > -threshold:
              mask = np.logical_and(mask, data >= vmin)
          if vmax < threshold:
              mask = np.logical_and(mask, data <= vmax)
      return mask
  
imports: import itertools
  import math
  from collections.abc import Sequence
  from warnings import warn
  import matplotlib
  import matplotlib.pyplot
  import numpy
  from matplotlib import gridspec
  from matplotlib.cm import ScalarMappable
  from matplotlib.colorbar import make_axes
  from matplotlib.colors import LinearSegmentedColormap, Normalize, to_rgba
  from matplotlib.patches import Patch
  from mpl_toolkits.mplot3d.art3d import Poly3DCollection
  from nilearn import image, surface
  from nilearn._utils import check_niimg_3d, compare_version, fill_doc
  from nilearn._utils.helpers import is_kaleido_installed, is_plotly_installed
  from nilearn.plotting.cm import cold_hot, mix_colormaps
  from nilearn.plotting.displays._slicers import _get_cbar_ticks
  from nilearn.plotting.html_surface import get_vertexcolor
  from nilearn.plotting.img_plotting import get_colorbar_and_data_ranges
  from nilearn.plotting.js_plotting_utils import colorscale
  from nilearn.surface import load_surf_data, load_surf_mesh, vol_to_surf
  from nilearn.surface.surface import check_mesh
label_desc: Use image node_id 14 for calling a global function with example usage: _threshold(data, threshold, vmin, vmax) and returns: mask