code: def test_get_dense_tensor(self):
      # Inputs.
      vocabulary_size = 3
      # -1 values are ignored.
      input_a = np.array(
          [[2, -1, -1], [0, 1, -1]]  # example 0, ids [2]
      )  # example 1, ids [0, 1]
      input_b = np.array(
          [[0, -1, -1], [-1, -1, -1]]  # example 0, ids [0]
      )  # example 1, ids []
      input_features = {"aaa": input_a, "bbb": input_b}
  
      # Embedding variable.
      embedding_dimension = 2
      embedding_values = (
          (1.0, 2.0),  # id 0
          (3.0, 5.0),  # id 1
          (7.0, 11.0),  # id 2
      )
  
      def _initializer(shape, dtype, partition_info):
          self.assertAllEqual(
              (vocabulary_size, embedding_dimension), shape
          )
          self.assertEqual(dtypes.float32, dtype)
          self.assertIsNone(partition_info)
          return embedding_values
  
      # Expected lookup result, using combiner='mean'.
      expected_lookups_a = (
          # example 0:
          (7.0, 11.0),  # ids [2], embedding = [7, 11]
          # example 1:
          (
              2.0,
              3.5,
          ),  # ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
      )
      expected_lookups_b = (
          # example 0:
          (1.0, 2.0),  # ids [0], embedding = [1, 2]
          # example 1:
          (0.0, 0.0),  # ids [], embedding = [0, 0]
      )
  
      # Build columns.
      categorical_column_a = fc_lib.categorical_column_with_identity(
          key="aaa", num_buckets=vocabulary_size
      )
      categorical_column_b = fc_lib.categorical_column_with_identity(
          key="bbb", num_buckets=vocabulary_size
      )
      (
          embedding_column_a,
          embedding_column_b,
      ) = tpu_fc.shared_embedding_columns(
          [categorical_column_a, categorical_column_b],
          dimension=embedding_dimension,
          initializer=_initializer,
      )
  
      # Provide sparse input and get dense result.
      embedding_lookup_a = embedding_column_a._get_dense_tensor(
          fc._LazyBuilder(input_features)
      )
      embedding_lookup_b = embedding_column_b._get_dense_tensor(
          fc._LazyBuilder(input_features)
      )
  
      # Assert expected embedding variable and lookups.
      global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
      self.assertItemsEqual(
          ("embedding_weights:0",), tuple([v.name for v in global_vars])
      )
      embedding_var = global_vars[0]
      with _initialized_session():
          self.assertAllEqual(embedding_values, embedding_var)
          self.assertAllEqual(expected_lookups_a, embedding_lookup_a)
          self.assertAllEqual(expected_lookups_b, embedding_lookup_b)
  
imports: import numpy
  from tensorflow.python.client import session
  from tensorflow.python.feature_column import feature_column
  from tensorflow.python.feature_column import feature_column_lib
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import lookup_ops
  from tensorflow.python.ops import parsing_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  from tensorflow.python.tpu import feature_column
label_desc: Use image node_id 3 for calling the SharedEmbeddingColumnTest obj's underlying member method code with example usage: obj.test_get_dense_tensor() and returns: embedding_values