docstr: ivy.Container instance method variant of ivy.initialize_tucker. This
  method simply wraps the function, and so the docstring for
  ivy.initialize_tucker also applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      input tensor
  rank
      number of components
  modes
      modes to consider in the input tensor
  seed
      Used to create a random seed distribution
      when init == 'random'
  init
      initialization scheme for tucker decomposition.
  svd
      function to use to compute the SVD
  non_negative
      if True, non-negative factors are returned
  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
  
  Returns
  -------
  core
      initialized core tensor
  factors
      list of factors
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code: def initialize_tucker(
      self: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      rank: Union[Sequence[int], ivy.Container],
      modes: Union[Sequence[int], ivy.Container],
      /,
      *,
      init: Optional[
          Union[
              Literal["svd", "random"], ivy.TuckerTensor, ivy.Container
          ]
      ] = "svd",
      seed: Optional[Union[int, ivy.Container]] = None,
      svd: Optional[
          Union[Literal["truncated_svd"], ivy.Container]
      ] = "truncated_svd",
      non_negative: Optional[Union[bool, ivy.Container]] = False,
      mask: Optional[
          Union[ivy.Array, ivy.NativeArray, ivy.Container]
      ] = None,
      svd_mask_repeats: Optional[Union[int, ivy.Container]] = 5,
      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 instance method variant of ivy.initialize_tucker. This
      method simply wraps the function, and so the docstring for
      ivy.initialize_tucker also applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          input tensor
      rank
          number of components
      modes
          modes to consider in the input tensor
      seed
          Used to create a random seed distribution
          when init == 'random'
      init
          initialization scheme for tucker decomposition.
      svd
          function to use to compute the SVD
      non_negative
          if True, non-negative factors are returned
      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
  
      Returns
      -------
      core
          initialized core tensor
      factors
          list of factors
      """
      return self.static_initialize_tucker(
          self,
          rank,
          modes,
          seed=seed,
          init=init,
          svd=svd,
          non_negative=non_negative,
          mask=mask,
          svd_mask_repeats=svd_mask_repeats,
          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 32 for calling the _ContainerWithLinearAlgebraExperimental obj's underlying member method code with example usage: obj.initialize_tucker() and returns: self