code: def _positive_evals(L1, L2, L3, er=2e-7):
      """Helper function that identifies which voxels in a array have all
      eigenvalues significantly larger than zero
  
      Parameters
      ----------
      L1 : ndarray
          First independent variable of the integral.
      L2 : ndarray
          Second independent variable of the integral.
      L3 : ndarray
          Third independent variable of the integral.
      er : float, optional
          A eigenvalues is classified as larger than zero if it is larger than er
  
      Returns
      -------
      ind : boolean (n,)
          Array that marks the voxels that have all eigenvalues are larger than
          zero.
  
      """
      ind = np.logical_and(L1 > er, np.logical_and(L2 > er, L3 > er))
  
      return ind
  
imports: import numpy
  import warnings
  import scipy.optimize
  import dipy.core.sphere
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.reconst.dti import TensorFit, mean_diffusivity, from_lower_triangular, lower_triangular, decompose_tensor, MIN_POSITIVE_SIGNAL, nlls_fit_tensor, restore_fit_tensor
  from dipy.reconst.utils import dki_design_matrix
  from dipy.reconst.recspeed import local_maxima
  from dipy.reconst.base import ReconstModel
  from dipy.core.ndindex import ndindex
  from dipy.core.geometry import sphere2cart, cart2sphere, perpendicular_directions
  from dipy.core.optimize import PositiveDefiniteLeastSquares
  from dipy.data import get_sphere, get_fnames, load_sdp_constraints
  from dipy.reconst.vec_val_sum import vec_val_vect
  from dipy.core.gradients import check_multi_b
label_desc: Use image node_id 1 for calling a global function with example usage: _positive_evals(L1, L2, L3, er) and returns: ind