docstr: ivy.Container static method variant of ivy.tucker. This method
  simply wraps the function, and so the docstring for ivy.tucker also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      input tensor
  rank
      size of the core tensor, ``(len(ranks) == tensor.ndim)``
      if int, the same rank is used for all modes
  fixed_factors
      if not None, list of modes for which to keep the factors fixed.
      Only valid if a Tucker tensor is provided as init.
  n_iter_max
      maximum number of iteration
  init
      {'svd', 'random'}, or TuckerTensor optional
      if a TuckerTensor is provided, this is used for initialization
  svd
      str, default is 'truncated_svd'
      function to use to compute the SVD,
  seed
      Used to create a random seed distribution
      when init == 'random'
  mask
      array of booleans with the same shape as ``tensor`` should be 0 where
      the values are missing and 1 everywhere else. Note:  if tensor is
      sparse, then mask should also be sparse with a fill value of 1 (or
      True).
  svd_mask_repeats
      number of iterations for imputing the values in the SVD matrix when
      mask is not None
  tol
      tolerance: the algorithm stops when the variation in
      the reconstruction error is less than the tolerance
  verbose
      if True, different in reconstruction errors are returned at each
      iteration.
  
  return_errors
      Indicates whether the algorithm should return all reconstruction errors
      and computation time of each iteration or not
      Default: False
  
  Returns
  -------
       Container of ivy.TuckerTensors or ivy.TuckerTensors and
      container of reconstruction errors if return_errors is True.
  
  References
  ----------
  .. [1] tl.G.Kolda and B.W.Bader, "Tensor Decompositions and Applications",
  SIAM REVIEW, vol. 51, n. 3, pp. 455-500, 2009.
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code: def static_tucker(
      x: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      rank: Union[Sequence[int], ivy.Container],
      /,
      *,
      fixed_factors: Optional[
          Union[Sequence[int], ivy.Container]
      ] = None,
      n_iter_max: Optional[Union[int, ivy.Container]] = 100,
      init: Optional[
          Union[
              Literal["svd", "random"], ivy.TuckerTensor, ivy.Container
          ]
      ] = "svd",
      svd: Optional[
          Union[Literal["truncated_svd"], ivy.Container]
      ] = "truncated_svd",
      seed: Optional[Union[int, ivy.Container]] = None,
      mask: Optional[
          Union[ivy.Array, ivy.NativeArray, ivy.Container]
      ] = None,
      svd_mask_repeats: Optional[Union[int, ivy.Container]] = 5,
      tol: Optional[Union[float, ivy.Container]] = 10e-5,
      verbose: Optional[Union[bool, ivy.Container]] = False,
      return_errors: Optional[Union[bool, ivy.Container]] = False,
      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,
  ) -> Tuple[ivy.Container, Sequence[ivy.Container]]:
      """ivy.Container static method variant of ivy.tucker. This method
      simply wraps the function, and so the docstring for ivy.tucker also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          input tensor
      rank
          size of the core tensor, ``(len(ranks) == tensor.ndim)``
          if int, the same rank is used for all modes
      fixed_factors
          if not None, list of modes for which to keep the factors fixed.
          Only valid if a Tucker tensor is provided as init.
      n_iter_max
          maximum number of iteration
      init
          {'svd', 'random'}, or TuckerTensor optional
          if a TuckerTensor is provided, this is used for initialization
      svd
          str, default is 'truncated_svd'
          function to use to compute the SVD,
      seed
          Used to create a random seed distribution
          when init == 'random'
      mask
          array of booleans with the same shape as ``tensor`` should be 0 where
          the values are missing and 1 everywhere else. Note:  if tensor is
          sparse, then mask should also be sparse with a fill value of 1 (or
          True).
      svd_mask_repeats
          number of iterations for imputing the values in the SVD matrix when
          mask is not None
      tol
          tolerance: the algorithm stops when the variation in
          the reconstruction error is less than the tolerance
      verbose
          if True, different in reconstruction errors are returned at each
          iteration.
  
      return_errors
          Indicates whether the algorithm should return all reconstruction errors
          and computation time of each iteration or not
          Default: False
  
      Returns
      -------
           Container of ivy.TuckerTensors or ivy.TuckerTensors and
          container of reconstruction errors if return_errors is True.
  
      References
      ----------
      .. [1] tl.G.Kolda and B.W.Bader, "Tensor Decompositions and Applications",
      SIAM REVIEW, vol. 51, n. 3, pp. 455-500, 2009.
      """
      return ContainerBase.cont_multi_map_in_function(
          "tucker",
          x,
          rank,
          fixed_factors=fixed_factors,
          seed=seed,
          init=init,
          svd=svd,
          n_iter_max=n_iter_max,
          mask=mask,
          svd_mask_repeats=svd_mask_repeats,
          tol=tol,
          verbose=verbose,
          return_errors=return_errors,
          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 35 for calling the _ContainerWithLinearAlgebraExperimental obj's underlying member method code with example usage: obj.static_tucker() and returns: ContainerBase