code: def testWarmStartEmbeddingColumn(self):
      # Create old and new vocabs for embedding column "sc_vocab".
      prev_vocab_path = self._write_vocab(
          ["apple", "banana", "guava", "orange"], "old_vocab"
      )
      new_vocab_path = self._write_vocab(
          [
              "orange",
              "guava",
              "banana",
              "apple",
              "raspberry",
              "blueberry",
          ],
          "new_vocab",
      )
  
      # Save checkpoint from which to warm-start.
      with ops.Graph().as_default() as g:
          with self.session(graph=g) as sess:
              variable_scope.get_variable(
                  "input_layer/sc_vocab_embedding/embedding_weights",
                  initializer=[
                      [0.5, 0.4],
                      [1.0, 1.1],
                      [2.0, 2.2],
                      [3.0, 3.3],
                  ],
              )
              self._write_checkpoint(sess)
  
      def _partitioner(shape, dtype):  # pylint:disable=unused-argument
          # Partition each var into 2 equal slices.
          partitions = [1] * len(shape)
          partitions[0] = min(2, shape.dims[0].value)
          return partitions
  
      # Create feature columns.
      sc_vocab = fc.categorical_column_with_vocabulary_file(
          "sc_vocab", vocabulary_file=new_vocab_path, vocabulary_size=6
      )
      emb_vocab_column = fc.embedding_column(
          categorical_column=sc_vocab, dimension=2
      )
      all_deep_cols = [emb_vocab_column]
      # New graph, new session with warm-starting.
      with ops.Graph().as_default() as g:
          with self.session(graph=g) as sess:
              cols_to_vars = {}
              with variable_scope.variable_scope(
                  "", partitioner=_partitioner
              ):
                  # Create the variables.
                  fc.input_layer(
                      features=self._create_dummy_inputs(),
                      feature_columns=all_deep_cols,
                      cols_to_vars=cols_to_vars,
                  )
              vocab_info = ws_util.VocabInfo(
                  new_vocab=sc_vocab.vocabulary_file,
                  new_vocab_size=sc_vocab.vocabulary_size,
                  num_oov_buckets=sc_vocab.num_oov_buckets,
                  old_vocab=prev_vocab_path,
                  # Can't use constant_initializer with load_and_remap.  In practice,
                  # use a truncated normal initializer.
                  backup_initializer=init_ops.random_uniform_initializer(
                      minval=0.42, maxval=0.42
                  ),
              )
              ws_util.warm_start(
                  self.get_temp_dir(),
                  var_name_to_vocab_info={
                      ws_util._infer_var_name(
                          cols_to_vars[emb_vocab_column]
                      ): vocab_info
                  },
              )
              self.evaluate(variables.global_variables_initializer())
              # Verify weights were correctly warm-started. Var corresponding to
              # emb_vocab_column should be correctly warm-started after vocab
              # remapping. Missing values are filled in with the EmbeddingColumn's
              # initializer.
              self._assert_cols_to_vars(
                  cols_to_vars,
                  {
                      emb_vocab_column: [
                          np.array(
                              [[3.0, 3.3], [2.0, 2.2], [1.0, 1.1]]
                          ),
                          np.array(
                              [[0.5, 0.4], [0.42, 0.42], [0.42, 0.42]]
                          ),
                      ]
                  },
                  sess,
              )
  
imports: import os
  import numpy
  from tensorflow.python.checkpoint import checkpoint
  from tensorflow.python.feature_column import feature_column_lib
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  from tensorflow.python.training import checkpoint_utils
  from tensorflow.python.training import saver
  from tensorflow.python.training import warm_starting_util
label_desc: Use image node_id 35 for calling the WarmStartingUtilTest obj's underlying member method code with example usage: obj.testWarmStartEmbeddingColumn() and returns: partitions