def testWarmStartMoreSettingsNoPartitioning(self):
    # Create old and new vocabs for sparse 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")
    # Create feature columns.
    sc_hash = fc.categorical_column_with_hash_bucket(
        "sc_hash", hash_bucket_size=15)
    sc_keys = fc.categorical_column_with_vocabulary_list(
        "sc_keys", vocabulary_list=["a", "b", "c", "e"])
    sc_vocab = fc.categorical_column_with_vocabulary_file(
        "sc_vocab", vocabulary_file=new_vocab_path, vocabulary_size=6)
    all_linear_cols = [sc_hash, sc_keys, sc_vocab]

    # Save checkpoint from which to warm-start.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        variable_scope.get_variable(
            "linear_model/sc_hash/weights", shape=[15, 1], initializer=norms())
        sc_keys_weights = variable_scope.get_variable(
            "some_other_name", shape=[4, 1], initializer=rand())
        variable_scope.get_variable(
            "linear_model/sc_vocab/weights",
            initializer=[[0.5], [1.], [2.], [3.]])
        self._write_checkpoint(sess)
        prev_keys_val = sess.run(sc_keys_weights)

    # New graph, new session with warmstarting.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        cols_to_vars = self._create_linear_model(all_linear_cols,
                                                 partitioner=None)
        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
        )
        ws_settings = ws_util._WarmStartSettings(
            self.get_temp_dir(),
            vars_to_warmstart=".*(sc_keys|sc_vocab).*",
            var_name_to_vocab_info={
                ws_util._infer_var_name(cols_to_vars[sc_vocab]): vocab_info
            },
            var_name_to_prev_var_name={
                ws_util._infer_var_name(cols_to_vars[sc_keys]):
                    "some_other_name"
            })
        ws_util._warmstart(ws_settings)
        sess.run(variables.global_variables_initializer())
        # Verify weights were correctly warmstarted.  Var corresponding to
        # sc_hash should not be warm-started.  Var corresponding to sc_vocab
        # should be correctly warmstarted after vocab remapping.
        self._assert_cols_to_vars(cols_to_vars, {
            sc_keys: [prev_keys_val],
            sc_hash: [np.zeros([15, 1])],
            sc_vocab: [np.array([[3.], [2.], [1.], [0.5], [0.], [0.]])]
        }, sess)

def sequence_categorical_column_with_vocabulary_file(
    key, vocabulary_file, vocabulary_size=None, num_oov_buckets=0,
    default_value=None, dtype=dtypes.string):
  """A sequence of categorical terms where ids use a vocabulary file.

  Pass this to `embedding_column` or `indicator_column` to convert sequence
  categorical data into dense representation for input to sequence NN, such as
  RNN.

  Example:

  ```python
  states = sequence_categorical_column_with_vocabulary_file(
      key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
      num_oov_buckets=5)
  states_embedding = embedding_column(states, dimension=10)
  columns = [states_embedding]

  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  input_layer, sequence_length = sequence_input_layer(features, columns)

  rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
  outputs, state = tf.nn.dynamic_rnn(
      rnn_cell, inputs=input_layer, sequence_length=sequence_length)
  ```

  Args:
    key: A unique string identifying the input feature.
    vocabulary_file: The vocabulary file name.
    vocabulary_size: Number of the elements in the vocabulary. This must be no
      greater than length of `vocabulary_file`, if less than length, later
      values are ignored. If None, it is set to the length of `vocabulary_file`.
    num_oov_buckets: Non-negative integer, the number of out-of-vocabulary
      buckets. All out-of-vocabulary inputs will be assigned IDs in the range
      `[vocabulary_size, vocabulary_size+num_oov_buckets)` based on a hash of
      the input value. A positive `num_oov_buckets` can not be specified with
      `default_value`.
    default_value: The integer ID value to return for out-of-vocabulary feature
      values, defaults to `-1`. This can not be specified with a positive
      `num_oov_buckets`.
    dtype: The type of features. Only string and integer types are supported.

  Returns:
    A `_SequenceCategoricalColumn`.

  Raises:
    ValueError: `vocabulary_file` is missing or cannot be opened.
    ValueError: `vocabulary_size` is missing or < 1.
    ValueError: `num_oov_buckets` is a negative integer.
    ValueError: `num_oov_buckets` and `default_value` are both specified.
    ValueError: `dtype` is neither string nor integer.
  """
  return fc_old._SequenceCategoricalColumn(
      fc_old.categorical_column_with_vocabulary_file(
          key=key,
          vocabulary_file=vocabulary_file,
          vocabulary_size=vocabulary_size,
          num_oov_buckets=num_oov_buckets,
          default_value=default_value,
          dtype=dtype))

  def testWarmStart_SparseColumnVocabulary(self):
    # Create vocab for sparse column "sc_vocab".
    vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"],
                                   "vocab")
    # Create feature column.
    sc_vocab = fc.categorical_column_with_vocabulary_file(
        "sc_vocab", vocabulary_file=vocab_path, vocabulary_size=4)

    # Save checkpoint from which to warm-start.
    _, prev_vocab_val = self._create_prev_run_var(
        "linear_model/sc_vocab/weights", shape=[4, 1], initializer=ones())

    partitioner = lambda shape, dtype: [1] * len(shape)
    # New graph, new session WITHOUT warm-starting.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([sc_vocab], partitioner)
        sess.run(variables.global_variables_initializer())
        # Without warm-starting, the weights should be initialized using default
        # initializer (which is init_ops.zeros_initializer).
        self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [np.zeros([4, 1])]},
                                  sess)

    # New graph, new session with warm-starting.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([sc_vocab], partitioner)
        # Since old vocab is not explicitly set in WarmStartSettings, the old
        # vocab is assumed to be same as new vocab.
        ws_util.warm_start(
            self.get_temp_dir(), vars_to_warm_start=".*sc_vocab.*")
        sess.run(variables.global_variables_initializer())
        # Verify weights were correctly warm-started.
        self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [prev_vocab_val]},
                                  sess)

  def testWarmStartInputLayerEmbeddingColumn(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.test_session(graph=g) as sess:
        _ = variable_scope.get_variable(
            "input_layer/sc_vocab_embedding/embedding_weights",
            initializer=[[0.5, 0.4], [1., 1.1], [2., 2.2], [3., 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[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 = fc.embedding_column(
        categorical_column=sc_vocab,
        dimension=2,
        # Can't use constant_initializer with load_and_remap.  In practice,
        # use a truncated normal initializer.
        initializer=init_ops.random_uniform_initializer(
            minval=0.42, maxval=0.42))
    all_deep_cols = [emb_vocab]
    # New graph, new session with warmstarting.
    with ops.Graph().as_default() as g:
      with self.test_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)
        ws_settings = ws_util._WarmStartSettings(
            self.get_temp_dir(), col_to_prev_vocab={
                emb_vocab: prev_vocab_path
            })
        ws_util._warmstart_input_layer(cols_to_vars, ws_settings)
        sess.run(variables.global_variables_initializer())
        # Verify weights were correctly warmstarted. Var corresponding to
        # emb_vocab should be correctly warmstarted after vocab remapping.
        # Missing values are filled in with the EmbeddingColumn's initializer.
        self._assert_cols_to_vars(
            cols_to_vars, {
                emb_vocab: [
                    np.array([[3., 3.3], [2., 2.2], [1., 1.1]]),
                    np.array([[0.5, 0.4], [0.42, 0.42], [0.42, 0.42]])
                ]
            }, sess)

  def testWarmStartInputLayerMoreSettings(self):
    # Create old and new vocabs for sparse 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")
    # Create feature columns.
    sc_hash = fc.categorical_column_with_hash_bucket(
        "sc_hash", hash_bucket_size=15)
    sc_keys = fc.categorical_column_with_vocabulary_list(
        "sc_keys", vocabulary_list=["a", "b", "c", "e"])
    sc_vocab = fc.categorical_column_with_vocabulary_file(
        "sc_vocab", vocabulary_file=new_vocab_path, vocabulary_size=6)
    all_linear_cols = [sc_hash, sc_keys, sc_vocab]

    # Save checkpoint from which to warm-start.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        _ = variable_scope.get_variable(
            "linear_model/sc_hash/weights", shape=[15, 1], initializer=norms())
        sc_keys_weights = variable_scope.get_variable(
            "some_other_name", shape=[4, 1], initializer=rand())
        _ = variable_scope.get_variable(
            "linear_model/sc_vocab/weights",
            initializer=[[0.5], [1.], [2.], [3.]])
        self._write_checkpoint(sess)
        prev_keys_val = sess.run(sc_keys_weights)

    def _partitioner(shape, dtype):  # pylint:disable=unused-argument
      # Partition each var into 2 equal slices.
      partitions = [1] * len(shape)
      partitions[0] = min(2, shape[0].value)
      return partitions

    # New graph, new session with warmstarting.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        cols_to_vars = self._create_linear_model(all_linear_cols, _partitioner)
        ws_settings = ws_util._WarmStartSettings(
            self.get_temp_dir(),
            col_to_prev_vocab={sc_vocab: prev_vocab_path},
            col_to_prev_tensor={sc_keys: "some_other_name"},
            exclude_columns=[sc_hash])
        ws_util._warmstart_input_layer(cols_to_vars, ws_settings)
        sess.run(variables.global_variables_initializer())
        # Verify weights were correctly warmstarted.  Var corresponding to
        # sc_hash should not be warm-started.  Var corresponding to sc_vocab
        # should be correctly warmstarted after vocab remapping.
        self._assert_cols_to_vars(cols_to_vars, {
            sc_keys:
                np.split(prev_keys_val, 2),
            sc_hash: [np.zeros([8, 1]), np.zeros([7, 1])],
            sc_vocab: [
                np.array([[3.], [2.], [1.]]),
                np.array([[0.5], [0.], [0.]])
            ]
        }, sess)

  def testWarmStart_SparseColumnVocabularyConstrainedVocabSizes(self):
    # Create old vocabulary, and use a size smaller than the total number of
    # entries.
    old_vocab_path = self._write_vocab(["apple", "guava", "banana"],
                                       "old_vocab")
    old_vocab_size = 2  # ['apple', 'guava']

    # Create new vocab for sparse column "sc_vocab".
    current_vocab_path = self._write_vocab(
        ["apple", "banana", "guava", "orange"], "current_vocab")
    # Create feature column.  Only use 2 of the actual entries, resulting in
    # ['apple', 'banana'] for the new vocabulary.
    sc_vocab = fc.categorical_column_with_vocabulary_file(
        "sc_vocab", vocabulary_file=current_vocab_path, vocabulary_size=2)

    # Save checkpoint from which to warm-start.
    self._create_prev_run_var(
        "linear_model/sc_vocab/weights", shape=[2, 1], initializer=ones())

    partitioner = lambda shape, dtype: [1] * len(shape)
    # New graph, new session WITHOUT warmstarting.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([sc_vocab], partitioner)
        sess.run(variables.global_variables_initializer())
        # Without warmstarting, the weights should be initialized using default
        # initializer (which is init_ops.zeros_initializer).
        self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [np.zeros([2, 1])]},
                                  sess)

    # New graph, new session with warmstarting.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([sc_vocab], partitioner)
        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=old_vocab_path,
            old_vocab_size=old_vocab_size
        )
        warmstart_settings = ws_util._WarmStartSettings(
            ckpt_to_initialize_from=self.get_temp_dir(),
            vars_to_warmstart=".*sc_vocab.*",
            var_name_to_vocab_info={
                "linear_model/sc_vocab/weights": vocab_info
            })
        ws_util._warmstart(warmstart_settings)
        sess.run(variables.global_variables_initializer())
        # Verify weights were correctly warmstarted.  'banana' isn't in the
        # first two entries of the old vocabulary, so it's newly initialized.
        self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [[[1], [0]]]}, sess)

  def testWarmStartVarsToWarmstartIsNone(self):
    # Create old and new vocabs for sparse 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")
    # Create feature columns.
    sc_hash = fc.categorical_column_with_hash_bucket(
        "sc_hash", hash_bucket_size=15)
    sc_keys = fc.categorical_column_with_vocabulary_list(
        "sc_keys", vocabulary_list=["a", "b", "c", "e"])
    sc_vocab = fc.categorical_column_with_vocabulary_file(
        "sc_vocab", vocabulary_file=new_vocab_path, vocabulary_size=6)
    all_linear_cols = [sc_hash, sc_keys, sc_vocab]

    # Save checkpoint from which to warm-start.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        variable_scope.get_variable(
            "linear_model/sc_hash/weights", shape=[15, 1], initializer=norms())
        variable_scope.get_variable(
            "some_other_name", shape=[4, 1], initializer=rand())
        variable_scope.get_variable(
            "linear_model/sc_vocab/weights",
            initializer=[[0.5], [1.], [2.], [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[0].value)
      return partitions

    # New graph, new session with warm-starting.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        cols_to_vars = self._create_linear_model(all_linear_cols, _partitioner)
        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)
        ws_settings = ws_util.WarmStartSettings(
            self.get_temp_dir(),
            # The special value of None here will ensure that only the variable
            # specified in var_name_to_vocab_info (sc_vocab embedding) is
            # warm-started.
            vars_to_warm_start=None,
            var_name_to_vocab_info={
                ws_util._infer_var_name(cols_to_vars[sc_vocab]): vocab_info
            },
            # Even though this is provided, the None value for
            # vars_to_warm_start overrides the logic, and this will not be
            # warm-started.
            var_name_to_prev_var_name={
                ws_util._infer_var_name(cols_to_vars[sc_keys]):
                    "some_other_name"
            })
        ws_util._warm_start(ws_settings)
        sess.run(variables.global_variables_initializer())
        # Verify weights were correctly warm-started.  Var corresponding to
        # sc_vocab should be correctly warm-started after vocab remapping,
        # and neither of the other two should be warm-started..
        self._assert_cols_to_vars(cols_to_vars, {
            sc_keys: [np.zeros([2, 1]), np.zeros([2, 1])],
            sc_hash: [np.zeros([8, 1]), np.zeros([7, 1])],
            sc_vocab: [
                np.array([[3.], [2.], [1.]]),
                np.array([[0.5], [0.], [0.]])
            ]
        }, sess)

  def testWarmStart_MultipleCols(self):
    # Create vocab for sparse column "sc_vocab".
    vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"],
                                   "vocab")

    # Create feature columns.
    sc_int = fc.categorical_column_with_identity("sc_int", num_buckets=10)
    sc_hash = fc.categorical_column_with_hash_bucket(
        "sc_hash", hash_bucket_size=15)
    sc_keys = fc.categorical_column_with_vocabulary_list(
        "sc_keys", vocabulary_list=["a", "b", "c", "e"])
    sc_vocab = fc.categorical_column_with_vocabulary_file(
        "sc_vocab", vocabulary_file=vocab_path, vocabulary_size=4)
    real = fc.numeric_column("real")
    real_bucket = fc.bucketized_column(real, boundaries=[0., 1., 2., 3.])
    cross = fc.crossed_column([sc_keys, sc_vocab], hash_bucket_size=20)
    all_linear_cols = [sc_int, sc_hash, sc_keys, sc_vocab, real_bucket, cross]

    # Save checkpoint from which to warm-start.  Also create a bias variable,
    # so we can check that it's also warm-started.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        sc_int_weights = variable_scope.get_variable(
            "linear_model/sc_int/weights", shape=[10, 1], initializer=ones())
        sc_hash_weights = variable_scope.get_variable(
            "linear_model/sc_hash/weights", shape=[15, 1], initializer=norms())
        sc_keys_weights = variable_scope.get_variable(
            "linear_model/sc_keys/weights", shape=[4, 1], initializer=rand())
        sc_vocab_weights = variable_scope.get_variable(
            "linear_model/sc_vocab/weights", shape=[4, 1], initializer=ones())
        real_bucket_weights = variable_scope.get_variable(
            "linear_model/real_bucketized/weights",
            shape=[5, 1],
            initializer=norms())
        cross_weights = variable_scope.get_variable(
            "linear_model/sc_keys_X_sc_vocab/weights",
            shape=[20, 1],
            initializer=rand())
        bias = variable_scope.get_variable(
            "linear_model/bias_weights",
            shape=[1],
            initializer=rand())
        self._write_checkpoint(sess)
        (prev_int_val, prev_hash_val, prev_keys_val, prev_vocab_val,
         prev_bucket_val, prev_cross_val, prev_bias_val) = sess.run([
             sc_int_weights, sc_hash_weights, sc_keys_weights, sc_vocab_weights,
             real_bucket_weights, cross_weights, bias
         ])

    partitioner = lambda shape, dtype: [1] * len(shape)
    # New graph, new session WITHOUT warm-starting.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        cols_to_vars = self._create_linear_model(all_linear_cols, partitioner)
        sess.run(variables.global_variables_initializer())
        # Without warm-starting, all weights should be initialized using default
        # initializer (which is init_ops.zeros_initializer).
        self._assert_cols_to_vars(cols_to_vars, {
            sc_int: [np.zeros([10, 1])],
            sc_hash: [np.zeros([15, 1])],
            sc_keys: [np.zeros([4, 1])],
            sc_vocab: [np.zeros([4, 1])],
            real_bucket: [np.zeros([5, 1])],
            cross: [np.zeros([20, 1])],
        }, sess)

    # New graph, new session with warm-starting.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        cols_to_vars = self._create_linear_model(all_linear_cols, partitioner)
        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=vocab_path)
        ws_util._warm_start(
            ws_util.WarmStartSettings(
                self.get_temp_dir(),
                var_name_to_vocab_info={
                    "linear_model/sc_vocab/weights": vocab_info
                }))
        sess.run(variables.global_variables_initializer())
        # Verify weights were correctly warm-started.
        self._assert_cols_to_vars(cols_to_vars, {
            sc_int: [prev_int_val],
            sc_hash: [prev_hash_val],
            sc_keys: [prev_keys_val],
            sc_vocab: [prev_vocab_val],
            real_bucket: [prev_bucket_val],
            cross: [prev_cross_val],
            "bias": [prev_bias_val],
        }, sess)

  def testWarmStartEmbeddingColumnLinearModel(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.test_session(graph=g) as sess:
        variable_scope.get_variable(
            "linear_model/sc_vocab_embedding/embedding_weights",
            initializer=[[0.5, 0.4], [1., 1.1], [2., 2.2], [3., 3.3]])
        variable_scope.get_variable(
            "linear_model/sc_vocab_embedding/weights",
            initializer=[[0.69], [0.71]])
        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[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 = fc.embedding_column(
        categorical_column=sc_vocab,
        dimension=2)
    all_deep_cols = [emb_vocab]
    # New graph, new session with warm-starting.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        cols_to_vars = {}
        with variable_scope.variable_scope("", partitioner=_partitioner):
          # Create the variables.
          fc.linear_model(
              features=self._create_dummy_inputs(),
              feature_columns=all_deep_cols,
              cols_to_vars=cols_to_vars)

        # Construct the vocab_info for the embedding weight.
        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(),
            vars_to_warm_start=".*sc_vocab.*",
            var_name_to_vocab_info={
                "linear_model/sc_vocab_embedding/embedding_weights": vocab_info
            })
        sess.run(variables.global_variables_initializer())
        # Verify weights were correctly warm-started. Var corresponding to
        # emb_vocab 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: [
                    # linear weights part 0.
                    np.array([[0.69]]),
                    # linear weights part 1.
                    np.array([[0.71]]),
                    # embedding_weights part 0.
                    np.array([[3., 3.3], [2., 2.2], [1., 1.1]]),
                    # embedding_weights part 1.
                    np.array([[0.5, 0.4], [0.42, 0.42], [0.42, 0.42]])
                ]
            },
            sess)