class_docstr: Tensor Ring decomposition via recursive SVD
  
      Decomposes `input_tensor` into a sequence of order-3 tensors (factors) [1]_.
  
  Parameters
  ----------
  input_tensor : tensorly.tensor
  rank : Union[int, List[int]]
          maximum allowable TR rank of the factors
          if int, then this is the same for all the factors
          if int list, then rank[k] is the rank of the kth factor
  mode : int, default is 0
          index of the first factor to compute
  svd : str, default is 'truncated_svd'
      function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
  verbose : boolean, optional
          level of verbosity
  
  Returns
  -------
  factors : TR factors
            order-3 tensors of the TR decomposition
  
  References
  ----------
  .. [1] Qibin Zhao et al. "Tensor Ring Decomposition" arXiv preprint arXiv:1606.05535, (2016).
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code: def fit_transform(self, tensor):
      self.decomposition_ = tensor_ring(
          tensor,
          rank=self.rank,
          mode=self.mode,
          svd=self.svd,
          verbose=self.verbose,
      )
      return self.decomposition_
  
file_path: tensorly/tensorly/decomposition/_tr.py
imports: import tensorly
  from ._base_decomposition import DecompositionMixin
  from ..base import matricize
  from ..tr_tensor import validate_tr_rank, TRTensor
  from ..tenalg.svd import svd_interface
label_desc: Use image node_id 2 for calling the TensorRing obj's underlying member method code with example usage: obj.fit_transform(tensor) and returns: self