t3toolbox.uniform_tucker_tensor_train.ut3_to_t3
===============================================

.. py:function:: t3toolbox.uniform_tucker_tensor_train.ut3_to_t3(x_uniform: UniformTuckerTensorTrain, stack_t3s: bool = False, use_jax: bool = False) -> t3toolbox.tucker_tensor_train.TuckerTensorTrain

   Convert UniformTuckerTensorTrain to TuckerTensorTrain.

   .. rubric:: Examples

   >>> import numpy as np
   >>> import t3toolbox.tucker_tensor_train as t3
   >>> import t3toolbox.uniform_tucker_tensor_train as ut3
   >>> x = t3.t3_corewise_randn((14,15,16), (4,6,5), (1,3,2,1), stack_shape=(2,))
   >>> uniform_x = ut3.t3_to_ut3(x) # Convert t3 -> ut3
   >>> print(uniform_x.uniform_structure)
   (3, 16, 6, 3, (2,))
   >>> print(uniform_x.shape)
   [[14 14]
    [15 15]
    [16 16]]
   >>> print(uniform_x.tucker_ranks)
   [[4 4]
    [6 6]
    [5 5]]
   >>> print(uniform_x.tt_ranks)
   [[1 1]
    [3 3]
    [2 2]
    [1 1]]
   >>> all_x2 = ut3.ut3_to_t3(uniform_x) # Convert ut3 -> t3 without stacking
   >>> for x2i in all_x2: print(x2i.uniform_structure)
   ((14, 15, 16), (4, 6, 5), (1, 3, 2, 1), ())
   ((14, 15, 16), (4, 6, 5), (1, 3, 2, 1), ())
   >>> stacked_x2 = ut3.ut3_to_t3(uniform_x, stack_t3s=True) # with stacking
   >>> print(stacked_x2.uniform_structure)
   ((14, 15, 16), (4, 6, 5), (1, 3, 2, 1), (2,))
   >>> for B, B2 in zip(stacked_x2.tucker_cores, x.tucker_cores): print(np.linalg.norm(B - B2))
   0.0
   0.0
   0.0
   >>> for G, G2 in zip(stacked_x2.tt_cores, x.tt_cores): print(np.linalg.norm(G - G2))
   0.0
   0.0
   0.0