class_docstr: Decompose a tensor into a matrix in tt-format
  
  Parameters
  ----------
  tensor : tensorized matrix
      if your input matrix is of size (4, 9) and your tensorized_shape (2, 2, 3, 3)
      then tensor should be tl.reshape(matrix, (2, 2, 3, 3))
  rank : 'same', float or int tuple
      - if 'same' creates a decomposition with the same number of parameters as `tensor`
      - if float, creates a decomposition with `rank` x the number of parameters of `tensor`
      - otherwise, the actual rank to be used, e.g. (1, rank_2, ..., 1) of size tensor.ndim//2. Note that boundary conditions dictate that the first rank = last rank = 1.
  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
  -------
  tt_matrix
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code: def __init__(self, rank, svd="truncated_svd", verbose=False):
      self.rank = rank
      self.svd = svd
      self.verbose = verbose
  
file_path: tensorly/tensorly/decomposition/_tt.py
imports: import tensorly
  from ._base_decomposition import DecompositionMixin
  from ..tt_tensor import validate_tt_rank, TTTensor
  from ..tt_matrix import validate_tt_matrix_rank, TTMatrix
  from ..tenalg.svd import svd_interface
label_desc: Use image node_id 1 to create a new TensorTrain object from inherited base classes: DecompositionMixin with example: obj = TensorTrain(rank, svd, verbose)