code: def initialize_tucker(
      x: Union[ivy.Array, ivy.NativeArray],
      rank: Sequence[int],
      modes: Sequence[int],
      /,
      *,
      init: Optional[
          Union[Literal["svd", "random"], ivy.TuckerTensor]
      ] = "svd",
      seed: Optional[int] = None,
      svd: Optional[Literal["truncated_svd"]] = "truncated_svd",
      non_negative: Optional[bool] = False,
      mask: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      svd_mask_repeats: Optional[int] = 5,
  ) -> Tuple[ivy.Array, Sequence[ivy.Array]]:
      """Initialize core and factors used in `tucker`. The type of initialization
      is set using `init`. If `init == 'random'` then initialize factor matrices
      using `random_state`. If `init == 'svd'` then initialize the `m`th factor
      matrix using the `rank` left singular vectors of the `m`th unfolding of the
      input tensor.
  
      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
      """
      try:
          assert len(x.shape) >= 2
      except ValueError as e:
          raise ValueError(
              "expected x to have at least 2 dimensions but it has only"
              f" {len(x.shape)} dimension(s)"
          ) from e
  
      # Initialisation
      if init == "svd":
          factors = []
          for index, mode in enumerate(modes):
              mask_unfold = (
                  None if mask is None else ivy.unfold(mask, mode)
              )
              U, _, _ = _svd_interface(
                  ivy.unfold(x, mode),
                  n_eigenvecs=rank[index],
                  method=svd,
                  non_negative=non_negative,
                  mask=mask_unfold,
                  n_iter_mask_imputation=svd_mask_repeats,
                  # random_state=random_state,
              )
              factors.append(U)
  
          # The initial core approximation is needed here for the masking step
          core = multi_mode_dot(x, factors, modes=modes, transpose=True)
  
      elif init == "random":
          core = (
              ivy.random_uniform(
                  shape=[rank[index] for index in range(len(modes))],
                  dtype=x.dtype,
                  seed=seed,
              )
              + 0.01
          )
          factors = [
              ivy.random_uniform(
                  shape=(x.shape[mode], rank[index]),
                  dtype=x.dtype,
                  seed=seed,
              )
              for index, mode in enumerate(modes)
          ]
  
      else:
          (core, factors) = init
  
      if non_negative is True:
          factors = [ivy.abs(f) for f in factors]
          core = ivy.abs(core)
  
      return (core, factors)
  
imports: import logging
  from typing import Union, Optional, Tuple, List, Sequence, Literal
  import ivy
  from ivy.utils.backend import current_backend
  from ivy.func_wrapper import to_native_arrays_and_back, handle_out_argument, handle_nestable, handle_array_like_without_promotion, handle_array_function, handle_device, inputs_to_ivy_arrays, handle_backend_invalid
  from ivy.utils.exceptions import handle_exceptions
label_desc: Use image node_id 22 for calling a global function with example usage: initialize_tucker() and returns: core,  factors