code: def setUp(self):
      super(TPUEmbeddingForServingTest, self).setUp()
  
      self.embedding_values = np.array(
          list(range(32)), dtype=np.float64
      )
      self.initializer = init_ops_v2.Constant(self.embedding_values)
      # Embedding for video initialized to
      # 0 1 2 3
      # 4 5 6 7
      # ...
      self.table_video = tpu_embedding_v2_utils.TableConfig(
          vocabulary_size=8,
          dim=4,
          initializer=self.initializer,
          combiner="sum",
          name="video",
      )
      # Embedding for user initialized to
      # 0 1
      # 2 3
      # 4 5
      # 6 7
      # ...
      self.table_user = tpu_embedding_v2_utils.TableConfig(
          vocabulary_size=16,
          dim=2,
          initializer=self.initializer,
          combiner="mean",
          name="user",
      )
      self.feature_config = (
          tpu_embedding_v2_utils.FeatureConfig(
              table=self.table_video, name="watched"
          ),
          tpu_embedding_v2_utils.FeatureConfig(
              table=self.table_video, name="favorited"
          ),
          tpu_embedding_v2_utils.FeatureConfig(
              table=self.table_user, name="friends"
          ),
      )
  
      self.batch_size = 2
      self.data_batch_size = 4
  
      # One (global) batch of inputs
      # sparse tensor for watched:
      # row 0: 0
      # row 1: 0, 1
      # row 2: 0, 1
      # row 3: 1
      self.feature_watched_indices = [
          [0, 0],
          [1, 0],
          [1, 1],
          [2, 0],
          [2, 1],
          [3, 0],
      ]
      self.feature_watched_values = [0, 0, 1, 0, 1, 1]
      self.feature_watched_row_lengths = [1, 2, 2, 1]
      # sparse tensor for favorited:
      # row 0: 0, 1
      # row 1: 1
      # row 2: 0
      # row 3: 0, 1
      self.feature_favorited_indices = [
          [0, 0],
          [0, 1],
          [1, 0],
          [2, 0],
          [3, 0],
          [3, 1],
      ]
      self.feature_favorited_values = [0, 1, 1, 0, 0, 1]
      self.feature_favorited_row_lengths = [2, 1, 1, 2]
      # sparse tensor for friends:
      # row 0: 3
      # row 1: 0, 1, 2
      # row 2: 3
      # row 3: 0, 1, 2
      self.feature_friends_indices = [
          [0, 0],
          [1, 0],
          [1, 1],
          [1, 2],
          [2, 0],
          [3, 0],
          [3, 1],
          [3, 2],
      ]
      self.feature_friends_values = [3, 0, 1, 2, 3, 0, 1, 2]
      self.feature_friends_row_lengths = [1, 3, 1, 3]
  
imports: import numpy
  from tensorflow.python.compat import v2_compat
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.ops import init_ops_v2
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.platform import test
  from tensorflow.python.tpu import tpu_embedding_for_serving
  from tensorflow.python.tpu import tpu_embedding_v2_utils
  from tensorflow.python.util import nest
label_desc: Use image node_id 1 for calling the TPUEmbeddingForServingTest obj's underlying member method code with example usage: obj.setUp() without return types