code: def threshold_matrix(K):
      r"""Remove negative eigenvalues from the given kernel matrix.
  
      This method yields the closest positive semi-definite matrix in
      any unitarily invariant norm, e.g. the Frobenius norm.
  
      Args:
          K (array[float]): Kernel matrix, assumed to be symmetric.
  
      Returns:
          array[float]: Kernel matrix with cropped negative eigenvalues.
  
      **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 threshold/truncate the eigenvalues of the matrix via
  
      .. code-block :: pycon
  
          >>> K_thresh = qml.kernels.threshold_matrix(K)
          >>> np.linalg.eigvalsh(K_thresh)
          array([0.,  1.,  2.])
  
      If the input matrix does not have negative eigenvalues, ``threshold_matrix``
      does not have any effect.
      """
      w, v = np.linalg.eigh(K)
  
      if w[0] < 0:
          # Transform spectrum: Threshold/clip at 0.
          w0 = np.clip(w, 0, None)
  
          return (v * w0) @ np.transpose(v)
  
      return K
  
imports: import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: threshold_matrix(K) and returns: K, unknown