code: def test_deterministic_initialization_from_seed(self):
      seed = test_util.test_seed(sampler_type="stateless")
      op = trainable_linear_operators.build_trainable_linear_operator_block(
          operators=(
              tf.linalg.LinearOperatorDiag,
              tf.linalg.LinearOperatorLowerTriangular,
          ),
          block_dims=(2, 3),
          batch_shape=[3],
          dtype=tf.float32,
          seed=seed,
      )
      self.evaluate([v.initializer for v in op.trainable_variables])
      op2 = trainable_linear_operators.build_trainable_linear_operator_block(
          operators=(
              tf.linalg.LinearOperatorDiag,
              tf.linalg.LinearOperatorLowerTriangular,
          ),
          block_dims=(2, 3),
          batch_shape=[3],
          dtype=tf.float32,
          seed=seed,
      )
      self.evaluate([v.initializer for v in op2.trainable_variables])
      self.assertAllEqual(op.to_dense(), op2.to_dense())
  
imports: import functools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import scale_matvec_linear_operator
  from tensorflow_probability.python.experimental.util import deferred_module
  from tensorflow_probability.python.experimental.vi.util import trainable_linear_operators
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 7 for calling the TrainableLinearOperators obj's underlying member method code with example usage: obj.test_deterministic_initialization_from_seed() without return types