# Path: t3f/shapes.py
# def tt_ranks(tt, name='t3f_tt_ranks'):
# def shape(tt, name='t3f_shape'):
# def raw_shape(tt, name='t3f_raw_shape'):
# def batch_size(tt, name='t3f_batch_size'):
# def lazy_tt_ranks(tt, name='t3f_lazy_tt_ranks'):
# def lazy_shape(tt, name='t3f_lazy_shape'):
# def lazy_raw_shape(tt, name='t3f_lazy_raw_shape'):
# def lazy_batch_size(tt, name='t3f_lazy_batch_size'):
# def clean_raw_shape(shape, name='t3f_clean_raw_shape'):
# def is_batch_broadcasting_possible(tt_a, tt_b):
# def squeeze_batch_dim(tt, name='t3f_squeeze_batch_dim'):
# def expand_batch_dim(tt, name='t3f_expand_batch_dim'):
# 
# Path: t3f/decompositions.py
# def to_tt_matrix(mat, shape, max_tt_rank=10, epsilon=None,
#                  name='t3f_to_tt_matrix'):
# def to_tt_tensor(tens, max_tt_rank=10, epsilon=None,
#                  name='t3f_to_tt_tensor'):
# def round(tt, max_tt_rank=None, epsilon=None, name='t3f_round'):
# def _round_tt(tt, max_tt_rank, epsilon):
# def _round_batch_tt(tt, max_tt_rank, epsilon):
# def orthogonalize_tt_cores(tt, left_to_right=True,
#                            name='t3f_orthogonalize_tt_cores'):
# def _orthogonalize_tt_cores_left_to_right(tt):
# def _orthogonalize_batch_tt_cores_left_to_right(tt):
# def _orthogonalize_tt_cores_right_to_left(tt):
# 
# Path: t3f/riemannian.py
# def project_sum(what, where, weights=None):
# def project(what, where):
# def project_matmul(what, where, matrix):
# def pairwise_flat_inner_projected(projected_tt_vectors_1,
#                                   projected_tt_vectors_2):
# def add_n_projected(tt_objects, coef=None):
#   def slice_tt_core(tt_core, left_idx, right_idx):
# def tangent_space_to_deltas(tt, name='t3f_tangent_space_to_deltas'):
# def deltas_to_tangent_space(deltas, tt, left=None, right=None,
#                             name='t3f_deltas_to_tangent_space'):
# 
# Path: t3f/utils.py
# def unravel_index(indices, shape):
# def replace_tf_svd_with_np_svd():
#   def my_svd(tensor, full_matrices=False, compute_uv=True):
# def in_eager_mode():


import tensorflow as tf
from t3f import shapes
from t3f import decompositions
from t3f import riemannian
from t3f import utils
    return wrong results!

    Example:
        # Quadratic form with matrix A: <x, A x>.
        # It's gradient is (A + A.T) x, it's Hessian is (A + A.T)
        # It's Riemannian Hessian by vector product is
        #     proj_vec = t3f.project(vector, x)
        #     t3f.project(t3f.matmul(A + t3f.transpose(A), proj_vec), x)
        f = lambda x: t3f.bilinear_form(A, x, x)
        res = t3f.hessian_vector_product(f, x, vector)

    Args:
        func: function that takes TensorTrain object as input and outputs a number.
        x: point at which to compute the Hessian and on which tangent space to
          project the gradient.
      vector: `TensorTrain` object which to multiply be the Hessian.
      name: string, name of the Op.
      runtime_check: [True] whether to do a sanity check that the passed
        function is invariant to different TT representations (otherwise
        the Rieamnnian gradient doesn't even exist). It makes things slower,
        but helps catching bugs, so turn it off during production deployment.

    Returns:
        `TensorTrain`, result of the Riemannian hessian by vector product.

    See also:
        t3f.gradients
    """
  all_cores = list(x.tt_cores) + list(vector.tt_cores)
  with tf.name_scope(name):

# Path: t3f/shapes.py
# def tt_ranks(tt, name='t3f_tt_ranks'):
# def shape(tt, name='t3f_shape'):
# def raw_shape(tt, name='t3f_raw_shape'):
# def batch_size(tt, name='t3f_batch_size'):
# def lazy_tt_ranks(tt, name='t3f_lazy_tt_ranks'):
# def lazy_shape(tt, name='t3f_lazy_shape'):
# def lazy_raw_shape(tt, name='t3f_lazy_raw_shape'):
# def lazy_batch_size(tt, name='t3f_lazy_batch_size'):
# def clean_raw_shape(shape, name='t3f_clean_raw_shape'):
# def is_batch_broadcasting_possible(tt_a, tt_b):
# def squeeze_batch_dim(tt, name='t3f_squeeze_batch_dim'):
# def expand_batch_dim(tt, name='t3f_expand_batch_dim'):
# 
# Path: t3f/decompositions.py
# def to_tt_matrix(mat, shape, max_tt_rank=10, epsilon=None,
#                  name='t3f_to_tt_matrix'):
# def to_tt_tensor(tens, max_tt_rank=10, epsilon=None,
#                  name='t3f_to_tt_tensor'):
# def round(tt, max_tt_rank=None, epsilon=None, name='t3f_round'):
# def _round_tt(tt, max_tt_rank, epsilon):
# def _round_batch_tt(tt, max_tt_rank, epsilon):
# def orthogonalize_tt_cores(tt, left_to_right=True,
#                            name='t3f_orthogonalize_tt_cores'):
# def _orthogonalize_tt_cores_left_to_right(tt):
# def _orthogonalize_batch_tt_cores_left_to_right(tt):
# def _orthogonalize_tt_cores_right_to_left(tt):
# 
# Path: t3f/riemannian.py
# def project_sum(what, where, weights=None):
# def project(what, where):
# def project_matmul(what, where, matrix):
# def pairwise_flat_inner_projected(projected_tt_vectors_1,
#                                   projected_tt_vectors_2):
# def add_n_projected(tt_objects, coef=None):
#   def slice_tt_core(tt_core, left_idx, right_idx):
# def tangent_space_to_deltas(tt, name='t3f_tangent_space_to_deltas'):
# def deltas_to_tangent_space(deltas, tt, left=None, right=None,
#                             name='t3f_deltas_to_tangent_space'):
# 
# Path: t3f/utils.py
# def unravel_index(indices, shape):
# def replace_tf_svd_with_np_svd():
#   def my_svd(tensor, full_matrices=False, compute_uv=True):
# def in_eager_mode():


# Path: t3f/autodiff.py
import tensorflow as tf
from t3f import shapes
from t3f import decompositions
from t3f import riemannian
from t3f import utils

    return wrong results!

    Example:
        # Quadratic form with matrix A: <x, A x>.
        # It's gradient is (A + A.T) x, it's Hessian is (A + A.T)
        # It's Riemannian Hessian by vector product is
        #     proj_vec = t3f.project(vector, x)
        #     t3f.project(t3f.matmul(A + t3f.transpose(A), proj_vec), x)
        f = lambda x: t3f.bilinear_form(A, x, x)
        res = t3f.hessian_vector_product(f, x, vector)

    Args:
        func: function that takes TensorTrain object as input and outputs a number.
        x: point at which to compute the Hessian and on which tangent space to
          project the gradient.
      vector: `TensorTrain` object which to multiply be the Hessian.
      name: string, name of the Op.
      runtime_check: [True] whether to do a sanity check that the passed
        function is invariant to different TT representations (otherwise
        the Rieamnnian gradient doesn't even exist). It makes things slower,
        but helps catching bugs, so turn it off during production deployment.

    Returns:
        `TensorTrain`, result of the Riemannian hessian by vector product.

    See also:
        t3f.gradients
    """
  all_cores = list(x.tt_cores) + list(vector.tt_cores)
  with tf.name_scope(name):

    left = decompositions.orthogonalize_tt_cores(x)