t3toolbox.weighted_tucker_tensor_train.wt3_entries
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.. py:function:: t3toolbox.weighted_tucker_tensor_train.wt3_entries(x: WeightedTuckerTensorTrain, index: t3toolbox.backend.common.NDArray, use_jax: bool = False) -> t3toolbox.backend.common.NDArray

   Compute an entry (or multiple entries) of a weighted Tucker tensor train.

   .. rubric:: Examples

   >>> import numpy as np
   >>> import t3toolbox.tucker_tensor_train as t3
   >>> import t3toolbox.weighted_tucker_tensor_train as wt3
   >>> randn = np.random.randn
   >>> x0 = t3.t3_corewise_randn((16,17,18), (5,6,7), (2,3,3,1), stack_shape=(4,))
   >>> tucker_vectors = tuple([randn(4, 5), randn(4, 6), randn(4, 7)])
   >>> tt_vectors = tuple([randn(4, 2), randn(4, 3), randn(4, 3), randn(4, 1)])
   >>> weights = wt3.EdgeVectors(tucker_vectors, tt_vectors)
   >>> x = wt3.WeightedTuckerTensorTrain(x0, weights)
   >>> index = [[9,0], [4,0], [7,0]] # get entries (9,4,7) and (0,0,0)
   >>> entries = wt3.wt3_entries(x, index)
   >>> x_dense = x.to_dense()
   >>> entries2 = np.moveaxis(np.array([x_dense[:, 9,4,7], x_dense[:, 0,0,0]]), 0,1)
   >>> print(np.linalg.norm(entries - entries2))
   2.8718552890331766e-14