code: def svd(x, /, *, full_matrices=True):
      """
      Returns a singular value decomposition (SVD) of a matrix (or a stack of matrices) x.
      """
      U, S, Vh = jax.numpy.linalg.svd(x, full_matrices=full_matrices)
      return SVDResult(U=U, S=S, Vh=Vh)
  
imports: from functools import partial
  from typing import NamedTuple
  import jax
  from jax.experimental.array_api._data_type_functions import _promote_to_default_dtype
label_desc: Use image node_id 18 for calling a global function with example usage: svd() and returns: SVDResult