docstr: ivy.Array instance method variant of ivy.make_svd_non_negative. This
  method simply wraps the function, and so the docstring for
  ivy.make_svd_non_negative also applies to this method with minimal
  changes.
  
  Parameters
  ----------
  self
      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.
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code: def make_svd_non_negative(
      self: 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]:
      """ivy.Array instance method variant of ivy.make_svd_non_negative. This
      method simply wraps the function, and so the docstring for
      ivy.make_svd_non_negative also applies to this method with minimal
      changes.
  
      Parameters
      ----------
      self
          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.
      """
      return ivy.make_svd_non_negative(
          self._data, U, S, V, nntype=nntype
      )
  
imports: import abc
  from typing import Optional, Union, Tuple, List, Sequence, Literal
  import ivy
label_desc: Use image node_id 13 for calling the _ArrayWithLinearAlgebraExperimental obj's underlying member method code with example usage: obj.make_svd_non_negative() and returns: ivy