docstr: ivy.Container static method variant of ivy.eigh_tridiagonal. This
  method simply wraps the function, and so the docstring for
  ivy.eigh_tridiagonal also applies to this method with minimal changes.
  
  Parameters
  ----------
  alpha
      An array or a container of real or complex arrays each of
      shape (n), the diagonal elements of the matrix.
  beta
      An array or a container of real or complex arrays each of shape (n-1),
      containing the elements of the first super-diagonal of the matrix.
  eigvals_only
      If False, both eigenvalues and corresponding eigenvectors are computed.
      If True, only eigenvalues are computed. Default is True.
  select
      Optional string with values in {'a', 'v', 'i'}
      (default is 'a') that determines which eigenvalues
      to calculate: 'a': all eigenvalues. 'v': eigenvalues
      in the interval (min, max] given by select_range.
      'i': eigenvalues with indices min <= i <= max.
  select_range
      Size 2 tuple or list or array specifying the range of
      eigenvalues to compute together with select. If select
      is 'a', select_range is ignored.
  tol
      Optional scalar. Ignored when backend is not Tensorflow. The
      absolute tolerance to which each eigenvalue is required. An
      eigenvalue (or cluster) is considered to have converged if
      it lies in an interval of this width. If tol is None (default),
      the value eps*|T|_2 is used where eps is the machine precision,
      and |T|_2 is the 2-norm of the matrix T.
  
  Returns
  -------
  eig_vals
      The eigenvalues of the matrix in non-decreasing order.
  eig_vectors
      If eigvals_only is False the eigenvectors are returned in the second
      output argument.
  
  Examples
  --------
  With :class:`ivy.Container` input:
  
  >>> alpha = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([2., 2., 2.]))
  >>> beta = ivy.array([0.,2.])
  >>> y = ivy.Container.static_eigh_tridiagonal(alpha, beta)
  >>> print(y)
  {
      a: ivy.array([-0.56155, 0., 3.56155]),
      b: ivy.array([0., 2., 4.])
  }
  
  >>> alpha = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([2., 2., 2.]))
  >>> beta = ivy.Container(a=ivy.array([0.,2.]), b=ivy.array([2.,2.]))
  >>> y = ivy.Container.static_eigh_tridiagonal(alpha, beta)
  >>> print(y)
  {
      a: ivy.array([-0.56155, 0., 3.56155]),
      b: ivy.array([-0.82842, 2., 4.82842])
  }
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code: def static_eigh_tridiagonal(
      alpha: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      beta: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      /,
      *,
      eigvals_only: Union[bool, ivy.Container] = True,
      select: Union[str, ivy.Container] = "a",
      select_range: Optional[
          Union[
              Tuple[int, int],
              List[int],
              ivy.Array,
              ivy.NativeArray,
              ivy.Container,
          ]
      ] = None,
      tol: Optional[Union[float, ivy.Container]] = None,
      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,
  ) -> Union[ivy.Container, Tuple[ivy.Container, ivy.Container]]:
      """ivy.Container static method variant of ivy.eigh_tridiagonal. This
      method simply wraps the function, and so the docstring for
      ivy.eigh_tridiagonal also applies to this method with minimal changes.
  
      Parameters
      ----------
      alpha
          An array or a container of real or complex arrays each of
          shape (n), the diagonal elements of the matrix.
      beta
          An array or a container of real or complex arrays each of shape (n-1),
          containing the elements of the first super-diagonal of the matrix.
      eigvals_only
          If False, both eigenvalues and corresponding eigenvectors are computed.
          If True, only eigenvalues are computed. Default is True.
      select
          Optional string with values in {'a', 'v', 'i'}
          (default is 'a') that determines which eigenvalues
          to calculate: 'a': all eigenvalues. 'v': eigenvalues
          in the interval (min, max] given by select_range.
          'i': eigenvalues with indices min <= i <= max.
      select_range
          Size 2 tuple or list or array specifying the range of
          eigenvalues to compute together with select. If select
          is 'a', select_range is ignored.
      tol
          Optional scalar. Ignored when backend is not Tensorflow. The
          absolute tolerance to which each eigenvalue is required. An
          eigenvalue (or cluster) is considered to have converged if
          it lies in an interval of this width. If tol is None (default),
          the value eps*|T|_2 is used where eps is the machine precision,
          and |T|_2 is the 2-norm of the matrix T.
  
      Returns
      -------
      eig_vals
          The eigenvalues of the matrix in non-decreasing order.
      eig_vectors
          If eigvals_only is False the eigenvectors are returned in the second
          output argument.
  
      Examples
      --------
      With :class:`ivy.Container` input:
  
      >>> alpha = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([2., 2., 2.]))
      >>> beta = ivy.array([0.,2.])
      >>> y = ivy.Container.static_eigh_tridiagonal(alpha, beta)
      >>> print(y)
      {
          a: ivy.array([-0.56155, 0., 3.56155]),
          b: ivy.array([0., 2., 4.])
      }
  
      >>> alpha = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([2., 2., 2.]))
      >>> beta = ivy.Container(a=ivy.array([0.,2.]), b=ivy.array([2.,2.]))
      >>> y = ivy.Container.static_eigh_tridiagonal(alpha, beta)
      >>> print(y)
      {
          a: ivy.array([-0.56155, 0., 3.56155]),
          b: ivy.array([-0.82842, 2., 4.82842])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "eigh_tridiagonal",
          alpha,
          beta,
          eigvals_only=eigvals_only,
          select=select,
          select_range=select_range,
          tol=tol,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
      )
  
imports: from typing import Union, Optional, List, Dict, Tuple, Sequence, Literal
  from ivy.data_classes.container.base import ContainerBase
  import ivy
label_desc: Use image node_id 1 for calling the _ContainerWithLinearAlgebraExperimental obj's underlying member method code with example usage: obj.static_eigh_tridiagonal() and returns: ContainerBase