code: def compute_lap_embedding_from_laplacian(
      L, num_proj_dimensions=None, proj_to_sphere=True
  ):
      """Returns the representation using Laplacian eigenvectors and in the unit cube."""
      num_proj_dimensions = (
          num_proj_dimensions
          if num_proj_dimensions is not None
          else L.shape[0]
      )  # return entire matrix.
      _, eigvecs = get_smallest_symm_eig(L, num_proj_dimensions - 1)
      if proj_to_sphere:  # each row has unit euclidean norm
          row_wise_norms = np.sqrt(np.sum(eigvecs**2, axis=1))
          sphere_repr = eigvecs / row_wise_norms[:, np.newaxis]
      # Pad with zeros
      if sphere_repr.shape[1] < num_proj_dimensions:
          zero_mat = np.zeros(
              (L.shape[0], num_proj_dimensions - sphere_repr.shape[1])
          )
          sphere_repr = np.concatenate((sphere_repr, zero_mat), axis=1)
      return sphere_repr
  
file_path: dragonfly/dragonfly/utils/graph_utils.py
imports: from copy import deepcopy
  import numpy
  from scipy.linalg import eigh
  from .general_utils import get_nonzero_indices_in_vector
label_desc: Use image node_id 6 for calling a global function with example usage: compute_lap_embedding_from_laplacian(L, num_proj_dimensions, proj_to_sphere) and returns: sphere_repr