TuckerTensorTrain.load
======================

.. py:method:: t3toolbox.tucker_tensor_train.TuckerTensorTrain.load(file, use_jax = False)
   :staticmethod:


   .. code-block:: python

      def 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 use_jax: If True, TuckerTensorTrain cores are jax arrays. If False, they are numpy arrays.
                   Default (``use_jax=False``): use numpy arrays.
   :type use_jax: bool, optional

   :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:meth:`.TuckerTensorTrain.save`

   .. rubric:: Examples

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