docstr: ivy.Container instance method variant of ivy.tensor_train. This
  method simply wraps the function, and so the docstring for
  ivy.tensor_train also applies to this method with minimal changes.
  
  Parameters
  ----------
  input_tensor
      tensor to be decomposed.
  rank
      maximum allowable TT-ranks of the decomposition.
  svd
      SVD method to use.
  verbose
      level of verbosity.
docstr_tok: ["ivy.Container","instance","method","variant","of","ivy.tensor_train",".","This","method","simply","wraps","the","function",",","and","so","the","docstring","for","ivy.tensor_train","also","applies","to","this","method","with","minimal","changes",".","Parameters","--","--","--","--","--","input_tensor","tensor","to","be","decomposed",".","rank","maximum","allowable","TT-ranks","of","the","decomposition",".","svd","SVD","method","to","use",".","verbose","level","of","verbosity","."]
code: def tensor_train(
      self: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      rank: Union[Sequence[int], ivy.Container],
      /,
      *,
      svd: Optional[
          Union[Literal["truncated_svd"], ivy.Container]
      ] = "truncated_svd",
      verbose: 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 instance method variant of ivy.tensor_train. This
      method simply wraps the function, and so the docstring for
      ivy.tensor_train also applies to this method with minimal changes.
  
      Parameters
      ----------
      input_tensor
          tensor to be decomposed.
      rank
          maximum allowable TT-ranks of the decomposition.
      svd
          SVD method to use.
      verbose
          level of verbosity.
      """
      return self.static_tensor_train(
          self,
          rank,
          svd=svd,
          verbose=verbose,
          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 28 for calling the _ContainerWithLinearAlgebraExperimental obj's underlying member method code with example usage: obj.tensor_train() and returns: self