code: def flip_matrix(K):
      r"""Remove negative eigenvalues from the given kernel matrix by taking the absolute value.
  
      This method keeps the eigenvectors of the matrix intact.
  
      Args:
          K (array[float]): Kernel matrix, assumed to be symmetric.
  
      Returns:
          array[float]: Kernel matrix with flipped negative eigenvalues.
  
      Reference:
          This method is introduced in
          `Wang, Du, Luo & Tao (2021) <https://doi.org/10.22331/q-2021-08-30-531>`_.
  
      **Example:**
  
      Consider a symmetric matrix with both positive and negative eigenvalues:
  
      .. code-block :: pycon
  
          >>> K = np.array([[0, 1, 0], [1, 0, 0], [0, 0, 2]])
          >>> np.linalg.eigvalsh(K)
          array([-1.,  1.,  2.])
  
      We then can invert the sign of all negative eigenvalues of the matrix, obtaining
      non-negative eigenvalues only:
  
      .. code-block :: pycon
  
          >>> K_flipped = qml.kernels.flip_matrix(K)
          >>> np.linalg.eigvalsh(K_flipped)
          array([1.,  1.,  2.])
  
      If the input matrix does not have negative eigenvalues, ``flip_matrix``
      does not have any effect.
      """
      w, v = np.linalg.eigh(K)
  
      if w[0] < 0:
          # Transform spectrum: absolute value
          w_abs = np.abs(w)
  
          return (v * w_abs) @ np.transpose(v)
  
      return K
  
imports: import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: flip_matrix(K) and returns: K, unknown