docstr: ivy.Container static method variant of ivy.qr. This method simply
  wraps the function, and so the docstring for ivy.qr also applies to
  this method with minimal changes.
  
  Returns the qr decomposition x = QR of a full column rank matrix (or a stack of
  matrices), where Q is an orthonormal matrix (or a stack of matrices) and R is an
  upper-triangular matrix (or a stack of matrices).
  
  Parameters
  ----------
  x
      input container having shape (..., M, N) and whose innermost two dimensions
      form MxN matrices of rank N. Should have a floating-point data type.
  mode
      decomposition mode. Should be one of the following modes:
      - 'reduced': compute only the leading K columns of q, such that q and r have
      dimensions (..., M, K) and (..., K, N), respectively, and where
      K = min(M, N).
      - 'complete': compute q and r with dimensions (..., M, M) and (..., M, N),
      respectively.
      Default: 'reduced'.
  key_chains
      The key-chains to apply or not apply the method to. Default is ``None``.
  to_apply
      If True, the method will be applied to key_chains, otherwise key_chains
      will be skipped. Default is ``True``.
  prune_unapplied
      Whether to prune key_chains for which the function was not applied.
      Default is ``False``.
  map_sequences
      Whether to also map method to sequences (lists, tuples).
      Default is ``False``.
  out
      optional output tuple of containers, for writing the result to. The arrays
      must have shapes that the inputs broadcast to.
  
  Returns
  -------
  ret
      a namedtuple (Q, R) whose
      - first element must have the field name Q and must be an container whose
      shape depends on the value of mode and contain matrices with orthonormal
      columns. If mode is 'complete', the container must have shape (..., M, M).
      If mode is 'reduced', the container must have shape (..., M, K), where
      K = min(M, N). The first x.ndim-2 dimensions must have the same size as
      those of the input container x.
      - second element must have the field name R and must be an container whose
      shape depends on the value of mode and contain upper-triangular matrices. If
      mode is 'complete', the container must have shape (..., M, N). If mode is
      'reduced', the container must have shape (..., K, N), where K = min(M, N).
      The first x.ndim-2 dimensions must have the same size as those of the input
      x.
  
  Examples
  --------
  >>> x = ivy.Container(a = ivy.native_array([[1., 2.], [3., 4.]]),
  ...                   b = ivy.array([[2., 3.], [4. ,5.]]))
  >>> q,r = ivy.Container.static_qr(x, mode='complete')
  >>> print(q)
  {
      a: ivy.array([[-0.31622777, -0.9486833],
                  [-0.9486833, 0.31622777]]),
      b: ivy.array([[-0.4472136, -0.89442719],
                  [-0.89442719, 0.4472136]])
  }
  >>> print(r)
  {
      a: ivy.array([[-3.16227766, -4.42718872],
                  [0., -0.63245553]]),
      b: ivy.array([[-4.47213595, -5.81377674],
                  [0., -0.4472136]])
  }
code: def _static_qr(
      x: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      /,
      *,
      mode: Union[str, ivy.Container] = "reduced",
      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[Tuple[ivy.Container, ivy.Container]] = None,
  ) -> Tuple[ivy.Container, ivy.Container]:
      """ivy.Container static method variant of ivy.qr. This method simply
      wraps the function, and so the docstring for ivy.qr also applies to
      this method with minimal changes.
  
      Returns the qr decomposition x = QR of a full column rank matrix (or a stack of
      matrices), where Q is an orthonormal matrix (or a stack of matrices) and R is an
      upper-triangular matrix (or a stack of matrices).
  
      Parameters
      ----------
      x
          input container having shape (..., M, N) and whose innermost two dimensions
          form MxN matrices of rank N. Should have a floating-point data type.
      mode
          decomposition mode. Should be one of the following modes:
          - 'reduced': compute only the leading K columns of q, such that q and r have
          dimensions (..., M, K) and (..., K, N), respectively, and where
          K = min(M, N).
          - 'complete': compute q and r with dimensions (..., M, M) and (..., M, N),
          respectively.
          Default: 'reduced'.
      key_chains
          The key-chains to apply or not apply the method to. Default is ``None``.
      to_apply
          If True, the method will be applied to key_chains, otherwise key_chains
          will be skipped. Default is ``True``.
      prune_unapplied
          Whether to prune key_chains for which the function was not applied.
          Default is ``False``.
      map_sequences
          Whether to also map method to sequences (lists, tuples).
          Default is ``False``.
      out
          optional output tuple of containers, for writing the result to. The arrays
          must have shapes that the inputs broadcast to.
  
      Returns
      -------
      ret
          a namedtuple (Q, R) whose
          - first element must have the field name Q and must be an container whose
          shape depends on the value of mode and contain matrices with orthonormal
          columns. If mode is 'complete', the container must have shape (..., M, M).
          If mode is 'reduced', the container must have shape (..., M, K), where
          K = min(M, N). The first x.ndim-2 dimensions must have the same size as
          those of the input container x.
          - second element must have the field name R and must be an container whose
          shape depends on the value of mode and contain upper-triangular matrices. If
          mode is 'complete', the container must have shape (..., M, N). If mode is
          'reduced', the container must have shape (..., K, N), where K = min(M, N).
          The first x.ndim-2 dimensions must have the same size as those of the input
          x.
  
      Examples
      --------
      >>> x = ivy.Container(a = ivy.native_array([[1., 2.], [3., 4.]]),
      ...                   b = ivy.array([[2., 3.], [4. ,5.]]))
      >>> q,r = ivy.Container.static_qr(x, mode='complete')
      >>> print(q)
      {
          a: ivy.array([[-0.31622777, -0.9486833],
                      [-0.9486833, 0.31622777]]),
          b: ivy.array([[-0.4472136, -0.89442719],
                      [-0.89442719, 0.4472136]])
      }
      >>> print(r)
      {
          a: ivy.array([[-3.16227766, -4.42718872],
                      [0., -0.63245553]]),
          b: ivy.array([[-4.47213595, -5.81377674],
                      [0., -0.4472136]])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "qr",
          x,
          mode=mode,
          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 33 for calling the _ContainerWithLinearAlgebra obj's underlying member method code with example usage: obj._static_qr() and returns: ContainerBase