docstr: ivy.Container static 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
  ----------
  tt_matrix
          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 container, for writing the result to.
  
  Returns
  -------
  output_tensor
              tensor whose TT-Matrix decomposition was given by 'factors'
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([[[[[0.49671414],
  ...                      [-0.1382643]],
  ...
  ...                     [[0.64768857],
  ...                      [1.5230298]]]],
  ...                   [[[[-0.23415337],
  ...                      [-0.23413695]],
  ...
  ...                     [[1.57921278],
  ...                      [0.76743472]]]]])))
  >>> y = ivy.Container.static_tt_matrix_to_tensor(x)
  >>> print(y["a"])
  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 static_tt_matrix_to_tensor(
      tt_matrix: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      /,
      *,
      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,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container static 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
      ----------
      tt_matrix
              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 container, for writing the result to.
  
      Returns
      -------
      output_tensor
                  tensor whose TT-Matrix decomposition was given by 'factors'
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([[[[[0.49671414],
      ...                      [-0.1382643]],
      ...
      ...                     [[0.64768857],
      ...                      [1.5230298]]]],
      ...                   [[[[-0.23415337],
      ...                      [-0.23413695]],
      ...
      ...                     [[1.57921278],
      ...                      [0.76743472]]]]])))
      >>> y = ivy.Container.static_tt_matrix_to_tensor(x)
      >>> print(y["a"])
      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 ContainerBase.cont_multi_map_in_function(
          "tt_matrix_to_tensor",
          tt_matrix,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          out=out,
      )
  
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 39 for calling the _ContainerWithLinearAlgebraExperimental obj's underlying member method code with example usage: obj.static_tt_matrix_to_tensor() and returns: ContainerBase