code: def test_larger_num_iters_improves_accuracy(self):
      # If last_iter_clip = True, this potentially computes lossy representation.
      # Set delta large, to measure the effect of changing num_iters on accuracy.
      x = np.random.randn(3, 12).astype(np.float32)
      errors = []
      seed = tf.constant([1, 2], tf.int64)
      for num_iters in [2, 3, 4, 5]:
          stage = kashin.KashinHadamardEncodingStage(
              num_iters=num_iters,
              eta=0.9,
              delta=100.0,
              last_iter_clip=True,
          )
          encode_params, decode_params = stage.get_params()
          # To keep the experiment consistent, we always need to use fixed seed.
          encode_params[
              kashin.KashinHadamardEncodingStage.SEED_PARAMS_KEY
          ] = seed
          decode_params[
              kashin.KashinHadamardEncodingStage.SEED_PARAMS_KEY
          ] = seed
          encoded_x, decoded_x = self.encode_decode_x(
              stage, x, encode_params, decode_params
          )
          test_data = test_utils.TestData(x, encoded_x, decoded_x)
          test_data = self.evaluate_test_data(test_data)
          errors.append(
              np.linalg.norm(test_data.x - test_data.decoded_x)
          )
      for e1, e2 in zip(errors[:-1], errors[1:]):
          # The incurred error with less iterations should be greater.
          self.assertGreater(e1, e2)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.internal.tensor_encoding.stages.research import kashin
  from tensorflow_model_optimization.python.core.internal.tensor_encoding.testing import test_utils
label_desc: Use image node_id 10 for calling the KashinHadamardEncodingStageTest obj's underlying member method code with example usage: obj.test_larger_num_iters_improves_accuracy() without return types