code: def test_works_when_results_have_dynamic_shape(self):
      # Create a variable (and thus loss) with dynamically-shaped result.
      x = tf.Variable(
          initial_value=tf1.placeholder_with_default(
              [5.0, 3.0], shape=None
          )
      )
  
      num_steps = 10
      losses, grads = minimize(
          loss_fn=lambda: (x - 2.0) ** 2,
          num_steps=num_steps,
          # TODO(b/137299119) Replace with TF2 optimizer.
          optimizer=tf1.train.AdamOptimizer(0.1),
          trace_fn=lambda t: (t.loss, t.gradients),
          trainable_variables=[x],
      )
      with tf.control_dependencies([losses]):
          final_x = tf.identity(x)
  
      self.evaluate(tf1.global_variables_initializer())
      final_x_, losses_, grads_ = self.evaluate(
          (final_x, losses, grads)
      )
      self.assertAllEqual(final_x_.shape, [2])
      self.assertAllEqual(losses_.shape, [num_steps, 2])
      self.assertAllEqual(grads_[0].shape, [num_steps, 2])
  
imports: import collections
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python import optimizer
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.math.minimize import minimize
  from tensorflow_probability.python.math.minimize import minimize_stateless
label_desc: Use image node_id 4 for calling the MinimizeTests obj's underlying member method code with example usage: obj.test_works_when_results_have_dynamic_shape() without return types