code: def displace_matrix(K):
      r"""Remove negative eigenvalues from the given kernel matrix by adding a multiple
      of the identity matrix.
  
      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 eigenvalues offset by adding the identity.
  
      **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 shift all eigenvalues of the matrix by adding the identity matrix
      multiplied with the absolute value of the smallest (the most negative, that is)
      eigenvalue:
  
      .. code-block :: pycon
  
          >>> K_displaced = qml.kernels.displace_matrix(K)
          >>> np.linalg.eigvalsh(K_displaced)
          array([0.,  2.,  3.])
  
      If the input matrix does not have negative eigenvalues, ``displace_matrix``
      does not have any effect.
      """
      wmin = np.linalg.eigvalsh(K)[0]
  
      if wmin < 0:
          return K - np.eye(K.shape[0]) * wmin
  
      return K
  
imports: import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: displace_matrix(K) and returns: K, unknown