code: def make_svd_non_negative(
      x: Union[ivy.Array, ivy.NativeArray],
      U: Union[ivy.Array, ivy.NativeArray],
      S: Union[ivy.Array, ivy.NativeArray],
      V: Union[ivy.Array, ivy.NativeArray],
      /,
      *,
      nntype: Optional[Literal["nndsvd", "nndsvda"]] = "nndsvd",
  ) -> Tuple[ivy.Array, ivy.Array]:
      """Use NNDSVD method to transform SVD results into a non-negative form.
      This method leads to more efficient solving with NNMF [1].
  
      Parameters
      ----------
      x
          tensor being decomposed.
      U
          left singular matrix from SVD.
      S
          diagonal matrix from SVD.
      V
          right singular matrix from SVD.
      nntype
          whether to fill small values with 0.0 (nndsvd),
            or the tensor mean (nndsvda, default).
  
      [1]: Boutsidis & Gallopoulos. Pattern Recognition, 41(4): 1350-1362, 2008.
      """
      W = ivy.zeros_like(U)
      H = ivy.zeros_like(V)
  
      # The leading singular triplet is non-negative
      # so it can be used as is for initialization.
      W[:, 0] = ivy.sqrt(S[0]) * ivy.abs(U[:, 0])
      H[0, :] = ivy.sqrt(S[0]) * ivy.abs(V[0, :])
  
      for j in range(1, len(S)):
          a, b = U[:, j], V[j, :]
  
          # extract positive and negative parts of column vectors
          a_p, b_p = ivy.where(a < 0.0, 0, a), ivy.where(
              b < 0.0, 0.0, b
          )
          # a_p, b_p = ivy.clip(a, 0.0), ivy.clip(b, 0.0)
          # a_n, b_n = ivy.abs(ivy.clip(a, 0.0)), ivy.abs(ivy.clip(b, 0.0))
          a_n, b_n = ivy.abs(ivy.where(a > 0.0, 0.0, a)), ivy.abs(
              ivy.where(b > 0.0, 0.0, b)
          )
  
          # and their norms
          a_p_nrm, b_p_nrm = float(ivy.vector_norm(a_p)), float(
              ivy.vector_norm(b_p)
          )
          a_n_nrm, b_n_nrm = float(ivy.vector_norm(a_n)), float(
              ivy.vector_norm(b_n)
          )
  
          m_p, m_n = a_p_nrm * b_p_nrm, a_n_nrm * b_n_nrm
  
          # choose update
          if m_p > m_n:
              u = a_p / a_p_nrm
              v = b_p / b_p_nrm
              sigma = m_p
          else:
              u = a_n / a_n_nrm
              v = b_n / b_n_nrm
              sigma = m_n
  
          lbd = float(ivy.sqrt(S[j] * sigma))
          W[:, j] = lbd * u
          H[j, :] = lbd * v
  
      # After this point we no longer need H
      eps = ivy.finfo(x.dtype).min
  
      if nntype == "nndsvd":
          W = ivy.soft_thresholding(W, eps)
          H = ivy.soft_thresholding(H, eps)
      elif nntype == "nndsvda":
          avg = ivy.mean(x)
          W = ivy.where(eps > W, ivy.ones(ivy.shape(W)) * avg, W)
          H = ivy.where(eps > H, ivy.ones(ivy.shape(H)) * avg, H)
      else:
          raise ValueError(
              f'Invalid nntype parameter: got {nntype} instead of one of ("nndsvd",'
              ' "nndsvda")'
          )
  
      return W, H
  
imports: import logging
  from typing import Union, Optional, Tuple, List, Sequence, Literal
  import ivy
  from ivy.utils.backend import current_backend
  from ivy.func_wrapper import to_native_arrays_and_back, handle_out_argument, handle_nestable, handle_array_like_without_promotion, handle_array_function, handle_device, inputs_to_ivy_arrays, handle_backend_invalid
  from ivy.utils.exceptions import handle_exceptions
label_desc: Use image node_id 18 for calling a global function with example usage: make_svd_non_negative() and returns: W,  H