docstr: Ivy.Array instance method variant of ivy.tt_matrix_to_tensor. This
  method simply wraps the function, and so the docstring for
  ivy.tt_matrix_to_tensor also applies to this method with minimal
  changes.
  
  Parameters
  ----------
  self
          array of 4D-arrays
          TT-Matrix factors (known as core) of shape
          (rank_k, left_dim_k, right_dim_k, rank_{k+1})
  
  out
      Optional output array. If provided, the output array to store the result.
  
  Returns
  -------
  output_tensor: array
              tensor whose TT-Matrix decomposition was given by 'factors'
   --------
   >>> a = ivy.array([[[[[0.49671414],
   ...                      [-0.1382643]],
   ...
   ...                     [[0.64768857],
   ...                      [1.5230298]]]],
   ...                   [[[[-0.23415337],
   ...                      [-0.23413695]],
   ...
   ...                     [[1.57921278],
   ...                      [0.76743472]]]]])
   >>> a.tt_matrix_to_tensor()
   ivy.array([[[[-0.1163073 , -0.11629914],
    [ 0.03237505,  0.03237278]],
  
   [[ 0.78441733,  0.38119566],
    [-0.21834874, -0.10610882]]],
  
  
  [[[-0.15165846, -0.15164782],
    [-0.35662258, -0.35659757]],
  
   [[ 1.02283812,  0.49705869],
    [ 2.40518808,  1.16882598]]]])
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code: def tt_matrix_to_tensor(
      self: Union[ivy.Array, ivy.NativeArray],
      /,
      *,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """Ivy.Array instance method variant of ivy.tt_matrix_to_tensor. This
      method simply wraps the function, and so the docstring for
      ivy.tt_matrix_to_tensor also applies to this method with minimal
      changes.
  
      Parameters
      ----------
      self
              array of 4D-arrays
              TT-Matrix factors (known as core) of shape
              (rank_k, left_dim_k, right_dim_k, rank_{k+1})
  
      out
          Optional output array. If provided, the output array to store the result.
  
      Returns
      -------
      output_tensor: array
                  tensor whose TT-Matrix decomposition was given by 'factors'
       --------
       >>> a = ivy.array([[[[[0.49671414],
       ...                      [-0.1382643]],
       ...
       ...                     [[0.64768857],
       ...                      [1.5230298]]]],
       ...                   [[[[-0.23415337],
       ...                      [-0.23413695]],
       ...
       ...                     [[1.57921278],
       ...                      [0.76743472]]]]])
       >>> a.tt_matrix_to_tensor()
       ivy.array([[[[-0.1163073 , -0.11629914],
        [ 0.03237505,  0.03237278]],
  
       [[ 0.78441733,  0.38119566],
        [-0.21834874, -0.10610882]]],
  
  
      [[[-0.15165846, -0.15164782],
        [-0.35662258, -0.35659757]],
  
       [[ 1.02283812,  0.49705869],
        [ 2.40518808,  1.16882598]]]])
      """
      return ivy.tt_matrix_to_tensor(self._data, out=out)
  
imports: import abc
  from typing import Optional, Union, Tuple, List, Sequence, Literal
  import ivy
label_desc: Use image node_id 19 for calling the _ArrayWithLinearAlgebraExperimental obj's underlying member method code with example usage: obj.tt_matrix_to_tensor() and returns: ivy