code: def mask_non_weighted_bvals(bvals, b0_threshold):
      """Create a diffusion gradient-weighting mask for the b-values according to
      the provided b0 threshold value.
  
      Parameters
      ----------
      bvals : (N,) ndarray
          The b-value, or magnitude, of each gradient direction.
      b0_threshold : float
          Gradients with b-value less than or equal to `b0_threshold` are
          considered to not have diffusion weighting.
  
      Returns
      -------
      ndarray
          Gradient-weighting mask: True for all b-value indices whose value is
          smaller or equal to ``b0_threshold``; False otherwise.
      """
  
      return bvals <= b0_threshold
  
imports: import logging
  from warnings import warn
  import numpy
  from scipy.linalg import inv, polar
  from dipy.io import gradients
  from dipy.core.onetime import auto_attr
  from dipy.core.geometry import vector_norm, vec2vec_rotmat
  from dipy.core.sphere import disperse_charges, HemiSphere
  from dipy.utils.deprecator import deprecate_with_version
label_desc: Use image node_id 4 for calling a global function with example usage: mask_non_weighted_bvals(bvals, b0_threshold) and returns: unknown