t3toolbox.tucker_tensor_train.t3_load
=====================================

.. py:function:: t3toolbox.tucker_tensor_train.t3_load(file, use_jax: bool = False) -> TuckerTensorTrain

   Load a Tucker tensor train from a file.

   :param file: Either the filename (string) or an open file (file-like object)
                where the data will be saved. If file is a string or a Path, the
                ``.npz`` extension will be appended to the filename if it is not
                already there.
   :type file: str or file
   :param xnp: Linear algebra backend. Default: np (numpy)

   :returns: Tucker tensor train loaded from the file
   :rtype: TuckerTensorTrain

   :raises RuntimeError: Error raised if the Tucker tensor train fails to load.
   :raises ValueError: Error raised if the Tucker tensor train fails is inconsistent.

   .. seealso:: :py:obj:`TuckerTensorTrain`, :py:obj:`t3_save`, :py:obj:`check_t3`

   .. rubric:: Examples

   >>> import numpy as np
   >>> import t3toolbox.tucker_tensor_train as t3
   >>> x = t3.t3_corewise_randn((14,15,16), (4,5,6), (1,3,2,1))
   >>> fname = 't3_file'
   >>> t3.t3_save(fname, x) # Save to file 't3_file.npz'
   >>> x2 = t3.t3_load(fname) # Load from file
   >>> tucker_cores, tt_cores = x.data
   >>> tucker_cores2, tt_cores2 = x2.data
   >>> print([np.linalg.norm(B - B2) for B, B2 in zip(tucker_cores, tucker_cores2)])
   [0.0, 0.0, 0.0]
   >>> print([np.linalg.norm(G - G2) for G, G2 in zip(tt_cores, tt_cores2)])
   [0.0, 0.0, 0.0]