code: def idzp_svd(eps, A):
      """
      Compute SVD of a complex matrix to a specified relative precision.
  
      :param eps:
          Relative precision.
      :type eps: float
      :param A:
          Matrix.
      :type A: :class:`numpy.ndarray`
  
      :return:
          Left singular vectors.
      :rtype: :class:`numpy.ndarray`
      :return:
          Right singular vectors.
      :rtype: :class:`numpy.ndarray`
      :return:
          Singular values.
      :rtype: :class:`numpy.ndarray`
      """
      A = np.asfortranarray(A)
      m, n = A.shape
      k, iU, iV, iS, w, ier = _id.idzp_svd(eps, A)
      if ier:
          raise _RETCODE_ERROR
      U = w[iU - 1 : iU + m * k - 1].reshape((m, k), order="F")
      V = w[iV - 1 : iV + n * k - 1].reshape((n, k), order="F")
      S = w[iS - 1 : iS + k - 1]
      return U, V, S
  
imports: import scipy.linalg._interpolative
  import numpy
label_desc: Use image node_id 43 for calling a global function with example usage: idzp_svd(eps, A) and returns: U,  V,  S