docstr: ivy.Container instance 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
  ----------
  self
      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
  --------
  >>> alpha = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([2., 2., 2.]))
  >>> beta = ivy.array([0.,2.])
  >>> y = alpha.eigh_tridiagonal(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 = alpha.eigh_tridiagonal(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 eigh_tridiagonal(
      self: 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 instance 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
      ----------
      self
          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
      --------
      >>> alpha = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([2., 2., 2.]))
      >>> beta = ivy.array([0.,2.])
      >>> y = alpha.eigh_tridiagonal(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 = alpha.eigh_tridiagonal(beta)
      >>> print(y)
      {
          a: ivy.array([-0.56155, 0., 3.56155]),
          b: ivy.array([-0.82842, 2., 4.82842])
      }
      """
      return self.static_eigh_tridiagonal(
          self,
          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 2 for calling the _ContainerWithLinearAlgebraExperimental obj's underlying member method code with example usage: obj.eigh_tridiagonal() and returns: self