# Path: t3f/initializers.py
# def _validate_input_parameters(is_tensor, shape, **params):
# def tensor_ones(shape, dtype=tf.float32, name='t3f_tensor_ones'):
# def tensor_zeros(shape, dtype=tf.float32, name='t3f_tensor_zeros'):
# def eye(shape, dtype=tf.float32, name='t3f_eye'):
# def matrix_ones(shape, dtype=tf.float32, name='t3f_matrix_ones'):
# def matrix_zeros(shape, dtype=tf.float32, name='t3f_matrix_zeros'):
# def tensor_with_random_cores(shape, tt_rank=2, mean=0., stddev=1.,
#                              dtype=tf.float32,
#                              name='t3f_tensor_with_random_cores'):
# def tensor_batch_with_random_cores(shape, tt_rank=2, batch_size=1,
#                                    mean=0., stddev=1., dtype=tf.float32,
#                                    name='t3f_tensor_batch_with_random_cores'):
# def matrix_with_random_cores(shape, tt_rank=2, mean=0., stddev=1.,
#                              dtype=tf.float32,
#                              name='t3f_matrix_with_random_cores'):
# def matrix_batch_with_random_cores(shape, tt_rank=2, batch_size=1,
#                                    mean=0., stddev=1., dtype=tf.float32,
#                                    name='t3f_matrix_batch_with_random_cores'):
# def ones_like(tt, name='t3f_ones_like'):
# def zeros_like(tt, name='t3f_zeros_like'):
# def random_tensor(shape, tt_rank=2, mean=0., stddev=1., dtype=tf.float32,
#                   name='t3f_random_tensor'):
# def random_tensor_batch(shape, tt_rank=2, batch_size=1, mean=0., stddev=1.,
#                         dtype=tf.float32, name='t3f_random_tensor_batch'):
# def random_matrix(shape, tt_rank=2, mean=0., stddev=1.,
#                   dtype=tf.float32, name='t3f_random_matrix'):
# def random_matrix_batch(shape, tt_rank=2, batch_size=1, mean=0., stddev=1.,
#                         dtype=tf.float32, name='t3f_random_matrix_batch'):
# def glorot_initializer(shape, tt_rank=2, dtype=tf.float32,
#                        name='t3f_glorot_initializer'):
# def he_initializer(shape, tt_rank=2, dtype=tf.float32,
#                    name='t3f_he_initializer'):
# def lecun_initializer(shape, tt_rank=2, dtype=tf.float32,
#                       name='t3f_lecun_initializer'):
# 
# 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'):


import tensorflow as tf
from t3f import initializers
from t3f import shapes
tf.compat.v1.enable_eager_execution()



class _ShapesTest():

  def testLazyShapeOverflow(self):

# Path: t3f/initializers.py
# def _validate_input_parameters(is_tensor, shape, **params):
# def tensor_ones(shape, dtype=tf.float32, name='t3f_tensor_ones'):
# def tensor_zeros(shape, dtype=tf.float32, name='t3f_tensor_zeros'):
# def eye(shape, dtype=tf.float32, name='t3f_eye'):
# def matrix_ones(shape, dtype=tf.float32, name='t3f_matrix_ones'):
# def matrix_zeros(shape, dtype=tf.float32, name='t3f_matrix_zeros'):
# def tensor_with_random_cores(shape, tt_rank=2, mean=0., stddev=1.,
#                              dtype=tf.float32,
#                              name='t3f_tensor_with_random_cores'):
# def tensor_batch_with_random_cores(shape, tt_rank=2, batch_size=1,
#                                    mean=0., stddev=1., dtype=tf.float32,
#                                    name='t3f_tensor_batch_with_random_cores'):
# def matrix_with_random_cores(shape, tt_rank=2, mean=0., stddev=1.,
#                              dtype=tf.float32,
#                              name='t3f_matrix_with_random_cores'):
# def matrix_batch_with_random_cores(shape, tt_rank=2, batch_size=1,
#                                    mean=0., stddev=1., dtype=tf.float32,
#                                    name='t3f_matrix_batch_with_random_cores'):
# def ones_like(tt, name='t3f_ones_like'):
# def zeros_like(tt, name='t3f_zeros_like'):
# def random_tensor(shape, tt_rank=2, mean=0., stddev=1., dtype=tf.float32,
#                   name='t3f_random_tensor'):
# def random_tensor_batch(shape, tt_rank=2, batch_size=1, mean=0., stddev=1.,
#                         dtype=tf.float32, name='t3f_random_tensor_batch'):
# def random_matrix(shape, tt_rank=2, mean=0., stddev=1.,
#                   dtype=tf.float32, name='t3f_random_matrix'):
# def random_matrix_batch(shape, tt_rank=2, batch_size=1, mean=0., stddev=1.,
#                         dtype=tf.float32, name='t3f_random_matrix_batch'):
# def glorot_initializer(shape, tt_rank=2, dtype=tf.float32,
#                        name='t3f_glorot_initializer'):
# def he_initializer(shape, tt_rank=2, dtype=tf.float32,
#                    name='t3f_he_initializer'):
# def lecun_initializer(shape, tt_rank=2, dtype=tf.float32,
#                       name='t3f_lecun_initializer'):
# 
# 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/shapes_test.py
import tensorflow as tf
from t3f import initializers
from t3f import shapes

tf.compat.v1.enable_eager_execution()



class _ShapesTest():

  def testLazyShapeOverflow(self):

    large_shape = [10] * 20