code: def _spectral(A, dim=2):
      # Input adjacency matrix A
      # Uses dense eigenvalue solver from numpy
      import numpy as np
  
      try:
          nnodes, _ = A.shape
      except AttributeError as err:
          msg = "spectral() takes an adjacency matrix as input"
          raise nx.NetworkXError(msg) from err
  
      # form Laplacian matrix where D is diagonal of degrees
      D = np.identity(nnodes, dtype=A.dtype) * np.sum(A, axis=1)
      L = D - A
  
      eigenvalues, eigenvectors = np.linalg.eig(L)
      # sort and keep smallest nonzero
      index = np.argsort(eigenvalues)[
          1 : dim + 1
      ]  # 0 index is zero eigenvalue
      return np.real(eigenvectors[:, index])
  
imports: import networkx
  from networkx.utils import np_random_state
label_desc: Use image node_id 13 for calling a global function with example usage: _spectral(A, dim) and returns: np