code: def _test_linear_operator_shape_and_gradients(
      self, op, batch_shape, domain_dim, range_dim, dtype
  ):
      x = tf.ones(domain_dim, dtype=dtype)
      with tf.GradientTape() as tape:
          y = op.matvec(x)
      grad = tape.gradient(y, op.trainable_variables)
      self.evaluate([v.initializer for v in op.trainable_variables])
  
      self.assertEqual(
          self.evaluate(op.domain_dimension_tensor()), domain_dim
      )
      self.assertEqual(
          self.evaluate(op.range_dimension_tensor()), range_dim
      )
      self.assertAllEqual(
          self.evaluate(op.batch_shape_tensor()),
          np.array(batch_shape, dtype=np.int32),
      )
      self.assertDTypeEqual(
          self.evaluate(op.to_dense()), dtype.as_numpy_dtype
      )
      self.assertNotEmpty(op.trainable_variables)
      self.assertAllNotNone(grad)
  
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 1 for calling the TrainableLinearOperators obj's underlying member method code with example usage: obj._test_linear_operator_shape_and_gradients(op, batch_shape, domain_dim, range_dim, dtype) without return types