code: def test_batch_sample(self, get_architecture):
      algorithm = categorical_harmonica.Harmonica(
          test_utils.create_spec(
              phoenix_spec_pb2.PhoenixSpec.DNN,
              blocks_to_use=[
                  "FIXED_CHANNEL_CONVOLUTION_16",
                  "FIXED_CHANNEL_CONVOLUTION_32",
                  "FIXED_CHANNEL_CONVOLUTION_64",
                  "CONVOLUTION_3X3",
              ],
              min_depth=4,
          ),
          degree=1,
      )
      get_architecture.side_effect = [
          np.array([1, 2, 3, 4]),
          np.array([2, 3, 4, 1]),
          np.array([3, 4, 1, 2]),
          # Adding 34 (flatten) to see that it is ignored
          np.array([4, 1, 2, 34, 3]),
          np.array([1, 1, 1, 1]),
          np.array([2, 2, 2, 2]),
          np.array([3, 3, 3, 3]),
          np.array([2, 3, 2, 3]),
          np.array([3, 4, 3, 4]),
      ]
      x, y = algorithm.batch_sample(_create_trials())
      # Check first and last assignments
      self.assertAllAlmostEqual(
          [
              1.00000000e00,
              1.00000000e00,
              1.00000000e00,
              1.00000000e00,
              1.00000000e00,
              6.12323400e-17,
              -1.00000000e00,
              -1.83697020e-16,
              1.00000000e00,
              -1.00000000e00,
              1.00000000e00,
              -1.00000000e00,
              1.00000000e00,
              -1.83697020e-16,
              -1.00000000e00,
              5.51091060e-16,
          ],
          x[0],
      )
      self.assertAllAlmostEqual(
          [
              0.00000000e00,
              1.22464680e-16,
              -2.44929360e-16,
              3.67394040e-16,
              0.00000000e00,
              -1.00000000e00,
              3.67394040e-16,
              1.00000000e00,
              0.00000000e00,
              1.22464680e-16,
              -2.44929360e-16,
              3.67394040e-16,
              0.00000000e00,
              -1.00000000e00,
              3.67394040e-16,
              1.00000000e00,
          ],
          x[17],
      )
      self.assertAllEqual(
          [
              0.97,
              0.0,
              0.94,
              0.0,
              0.7,
              0.0,
              0.72,
              0.0,
              0.3,
              0.0,
              0.79,
              0.0,
              0.39,
              0.0,
              0.9,
              0.0,
              0.19,
              0.0,
          ],
          y,
      )
  
imports: from absl import logging
  from absl.testing import parameterized
  import mock
  from model_search import hparam
  from model_search.metadata import trial
  from model_search.proto import phoenix_spec_pb2
  from model_search.search import categorical_harmonica
  from model_search.search import test_utils
  import numpy
  from sklearn import preprocessing
  import tensorflow.compat.v2
label_desc: Use image node_id 4 for calling the HarmonicaTest obj's underlying member method code with example usage: obj.test_batch_sample(get_architecture) without return types