TuckerTensorTrain.segment
=========================

.. py:method:: t3toolbox.tucker_tensor_train.TuckerTensorTrain.segment(start, stop)

   .. code-block:: python

      def segment(
              self,
              start: int,  # requires stop > start
              stop:  int,  # requires stop > start
      ) -> 'TuckerTensorTrain':


   Extract contiguous segment of this TuckerTensorTrain. Segments must have length at least one.

   :param start: Starting index for segment. Requires ``stop > start``.
   :type start: int
   :param stop: Stopping index for segment. Requires ``stop > start``.
   :type stop: int

   :returns: Segment of this TuckerTensorTrain, with ``shape=(N(start), ..., N(stop-1))``.
   :rtype: TuckerTensorTrain

   :raises ValueError: If ``stop <= start``.

   .. seealso:: :py:meth:`.TuckerTensorTrain.concatenate`

   .. rubric:: Examples

   >>> import numpy as np
   >>> import t3toolbox.tucker_tensor_train as t3
   >>> randn = np.random.randn
   >>> tucker_cores = (randn(4,14), randn(5,15), randn(6,16), randn(7,17))
   >>> tt_cores = (randn(2,4,3), randn(3,5,2), randn(2,6,2), randn(2,7,4))
   >>> x = t3.TuckerTensorTrain(tucker_cores, tt_cores)
   >>> x01 = x.segment(1,3)
   >>> print(x01.core_shapes)
   (((5, 15), (6, 16)), ((3, 5, 2), (2, 6, 2)))