docstr: ivy.Container static method variant of ivy.svd. This method simply
  wraps the function, and so the docstring for ivy.svd also applies to
  this method with minimal changes.
  
  Parameters
  ----------
  x
      input container with array leaves having shape ``(..., M, N)`` and whose
      innermost two dimensions form matrices on which to perform singular value
      decomposition. Should have a floating-point data type.
  full_matrices
      If ``True``, compute full-sized ``U`` and ``Vh``, such that ``U`` has
      shape ``(..., M, M)`` and ``Vh`` has shape ``(..., N, N)``. If ``False``,
      compute on             the leading ``K`` singular vectors, such that ``U``
      has shape ``(..., M, K)`` and ``Vh`` has shape ``(..., K, N)`` and where
      ``K = min(M, N)``. Default: ``True``.
  compute_uv
      If ``True`` then left and right singular vectors will be computed and
      returned in ``U`` and ``Vh``, respectively. Otherwise, only the singular
      values will be computed, which can be significantly faster.
  .. note::
      with backend set as torch, svd with still compute left and right singular
      vectors irrespective of the value of compute_uv, however Ivy will
      still only return the
      singular values.
  
  Returns
  -------
  .. note::
      once complex numbers are supported, each square matrix must be Hermitian.
  
  ret
      A container of a namedtuples ``(U, S, Vh)``. More details in ivy.svd.
  
  
  Examples
  --------
  With :class:`ivy.Container` input:
  
  >>> x = ivy.random_normal(shape = (9, 6))
  >>> y = ivy.random_normal(shape = (2, 4))
  >>> z = ivy.Container(a=x, b=y)
  >>> ret = ivy.Container.static_svd(z)
  >>> aU, aS, aVh = ret.a
  >>> bU, bS, bVh = ret.b
  >>> print(aU.shape, aS.shape, aVh.shape, bU.shape, bS.shape, bVh.shape)
  (9, 9) (6,) (6, 6) (2, 2) (2,) (4, 4)
code: def _static_svd(
      x: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      /,
      *,
      compute_uv: Union[bool, ivy.Container] = True,
      full_matrices: Union[bool, ivy.Container] = True,
      key_chains: Optional[
          Union[List[str], Dict[str, str], ivy.Container]
      ] = None,
      to_apply: Union[bool, ivy.Container] = True,
      prune_unapplied: Union[bool, ivy.Container] = False,
      map_sequences: Union[bool, ivy.Container] = False,
      out: Optional[ivy.Container] = None,
  ) -> Union[ivy.Container, Tuple[ivy.Container, ...]]:
      """ivy.Container static method variant of ivy.svd. This method simply
      wraps the function, and so the docstring for ivy.svd also applies to
      this method with minimal changes.
  
      Parameters
      ----------
      x
          input container with array leaves having shape ``(..., M, N)`` and whose
          innermost two dimensions form matrices on which to perform singular value
          decomposition. Should have a floating-point data type.
      full_matrices
          If ``True``, compute full-sized ``U`` and ``Vh``, such that ``U`` has
          shape ``(..., M, M)`` and ``Vh`` has shape ``(..., N, N)``. If ``False``,
          compute on             the leading ``K`` singular vectors, such that ``U``
          has shape ``(..., M, K)`` and ``Vh`` has shape ``(..., K, N)`` and where
          ``K = min(M, N)``. Default: ``True``.
      compute_uv
          If ``True`` then left and right singular vectors will be computed and
          returned in ``U`` and ``Vh``, respectively. Otherwise, only the singular
          values will be computed, which can be significantly faster.
      .. note::
          with backend set as torch, svd with still compute left and right singular
          vectors irrespective of the value of compute_uv, however Ivy will
          still only return the
          singular values.
  
      Returns
      -------
      .. note::
          once complex numbers are supported, each square matrix must be Hermitian.
  
      ret
          A container of a namedtuples ``(U, S, Vh)``. More details in ivy.svd.
  
  
      Examples
      --------
      With :class:`ivy.Container` input:
  
      >>> x = ivy.random_normal(shape = (9, 6))
      >>> y = ivy.random_normal(shape = (2, 4))
      >>> z = ivy.Container(a=x, b=y)
      >>> ret = ivy.Container.static_svd(z)
      >>> aU, aS, aVh = ret.a
      >>> bU, bS, bVh = ret.b
      >>> print(aU.shape, aS.shape, aVh.shape, bU.shape, bS.shape, bVh.shape)
      (9, 9) (6,) (6, 6) (2, 2) (2,) (4, 4)
      """
      return ContainerBase.cont_multi_map_in_function(
          "svd",
          x,
          compute_uv=compute_uv,
          full_matrices=full_matrices,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          out=out,
      )
  
imports: from typing import Union, Optional, Tuple, Literal, List, Dict, Sequence
  from ivy.data_classes.container.base import ContainerBase
  import ivy
label_desc: Use image node_id 39 for calling the _ContainerWithLinearAlgebra obj's underlying member method code with example usage: obj._static_svd() and returns: ContainerBase