docstr: ivy.Array instance method variant of ivy.partial_tucker. This method
  simply wraps the function, and so the docstring for ivy.partial_tucker
  also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      the  input tensor
  rank
      size of the core tensor, ``(len(ranks) == tensor.ndim)``
      if int, the same rank is used for all modes
      if None, original tensors size will be preserved.
  modes
      list of the modes on which to perform the decomposition
  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_erros
      if True, list of reconstruction errors are returned.
  
  Returns
  -------
  core : ndarray
          core tensor of the Tucker decomposition
  factors : ndarray list
          list of factors of the Tucker decomposition.
          with ``core.shape[i] == (tensor.shape[i], ranks[i]) for i in modes``
docstr_tok: ["ivy.Array","instance","method","variant","of","ivy.partial_tucker",".","This","method","simply","wraps","the","function",",","and","so","the","docstring","for","ivy.partial_tucker","also","applies","to","this","method","with","minimal","changes",".","Parameters","--","--","--","--","--","self","the","input","tensor","rank","size","of","the","core","tensor",",","``","(","len","(","ranks",")","==","tensor.ndim",")","``","if","int",",","the","same","rank","is","used","for","all","modes","if","None",",","original","tensors","size","will","be","preserved",".","modes","list","of","the","modes","on","which","to","perform","the","decomposition","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_erros","if","True",",","list","of","reconstruction","errors","are","returned",".","Returns","--","--","--","-","core",":","ndarray","core","tensor","of","the","Tucker","decomposition","factors",":","ndarray","list","list","of","factors","of","the","Tucker","decomposition",".","with","``","core.shape","[","i","]","==","(","tensor.shape","[","i","]",",","ranks","[","i","]",")","for","i","in","modes","``"]
code: def partial_tucker(
      self: Union[ivy.Array, ivy.NativeArray],
      rank: Optional[Sequence[int]] = None,
      modes: Optional[Sequence[int]] = None,
      /,
      *,
      n_iter_max: Optional[int] = 100,
      init: Optional[
          Union[Literal["svd", "random"], ivy.TuckerTensor]
      ] = "svd",
      svd: Optional[Literal["truncated_svd"]] = "truncated_svd",
      seed: Optional[int] = None,
      mask: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      svd_mask_repeats: Optional[int] = 5,
      tol: Optional[float] = 10e-5,
      verbose: Optional[bool] = False,
      return_errors: Optional[bool] = False,
  ) -> Tuple[ivy.Array, Sequence[ivy.Array]]:
      """ivy.Array instance method variant of ivy.partial_tucker. This method
      simply wraps the function, and so the docstring for ivy.partial_tucker
      also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          the  input tensor
      rank
          size of the core tensor, ``(len(ranks) == tensor.ndim)``
          if int, the same rank is used for all modes
          if None, original tensors size will be preserved.
      modes
          list of the modes on which to perform the decomposition
      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_erros
          if True, list of reconstruction errors are returned.
  
      Returns
      -------
      core : ndarray
              core tensor of the Tucker decomposition
      factors : ndarray list
              list of factors of the Tucker decomposition.
              with ``core.shape[i] == (tensor.shape[i], ranks[i]) for i in modes``
      """
      return ivy.partial_tucker(
          self._data,
          rank,
          modes,
          n_iter_max=n_iter_max,
          init=init,
          svd=svd,
          seed=seed,
          mask=mask,
          svd_mask_repeats=svd_mask_repeats,
          tol=tol,
          verbose=verbose,
          return_errors=return_errors,
      )
  
imports: import abc
  from typing import Optional, Union, Tuple, List, Sequence, Literal
  import ivy
label_desc: Use image node_id 17 for calling the _ArrayWithLinearAlgebraExperimental obj's underlying member method code with example usage: obj.partial_tucker() and returns: ivy