def test_sequence_length_with_empty_rows(self):
    """Tests _sequence_length when some examples do not have ids."""
    vocabulary_size = 3
    sparse_input_a = sparse_tensor.SparseTensorValue(
        # example 0, ids []
        # example 1, ids [2]
        # example 2, ids [0, 1]
        # example 3, ids []
        # example 4, ids [1]
        # example 5, ids []
        indices=((1, 0), (2, 0), (2, 1), (4, 0)),
        values=(2, 0, 1, 1),
        dense_shape=(6, 2))
    expected_sequence_length_a = [0, 1, 2, 0, 1, 0]
    categorical_column_a = sfc.sequence_categorical_column_with_identity(
        key='aaa', num_buckets=vocabulary_size)

    sparse_input_b = sparse_tensor.SparseTensorValue(
        # example 0, ids [2]
        # example 1, ids []
        # example 2, ids []
        # example 3, ids []
        # example 4, ids [1]
        # example 5, ids [0, 1]
        indices=((0, 0), (4, 0), (5, 0), (5, 1)),
        values=(2, 1, 0, 1),
        dense_shape=(6, 2))
    expected_sequence_length_b = [1, 0, 0, 0, 1, 2]
    categorical_column_b = sfc.sequence_categorical_column_with_identity(
        key='bbb', num_buckets=vocabulary_size)

    shared_embedding_columns = fc.shared_embedding_columns(
        [categorical_column_a, categorical_column_b], dimension=2)

    sequence_length_a = shared_embedding_columns[0]._get_sequence_dense_tensor(
        _LazyBuilder({
            'aaa': sparse_input_a
        }))[1]
    sequence_length_b = shared_embedding_columns[1]._get_sequence_dense_tensor(
        _LazyBuilder({
            'bbb': sparse_input_b
        }))[1]

    with monitored_session.MonitoredSession() as sess:
      self.assertAllEqual(
          expected_sequence_length_a, sequence_length_a.eval(session=sess))
      self.assertAllEqual(
          expected_sequence_length_b, sequence_length_b.eval(session=sess))

  def test_get_sequence_dense_tensor(self):
    vocabulary_size = 3
    sparse_input = sparse_tensor.SparseTensorValue(
        # example 0, ids [2]
        # example 1, ids [0, 1]
        # example 2, ids []
        # example 3, ids [1]
        indices=((0, 0), (1, 0), (1, 1), (3, 0)),
        values=(2, 0, 1, 1),
        dense_shape=(4, 2))

    expected_lookups = [
        # example 0, ids [2]
        [[0., 0., 1.], [0., 0., 0.]],
        # example 1, ids [0, 1]
        [[1., 0., 0.], [0., 1., 0.]],
        # example 2, ids []
        [[0., 0., 0.], [0., 0., 0.]],
        # example 3, ids [1]
        [[0., 1., 0.], [0., 0., 0.]],
    ]

    categorical_column = sfc.sequence_categorical_column_with_identity(
        key='aaa', num_buckets=vocabulary_size)
    indicator_column = fc.indicator_column(categorical_column)

    indicator_tensor, _ = indicator_column._get_sequence_dense_tensor(
        _LazyBuilder({'aaa': sparse_input}))

    with monitored_session.MonitoredSession() as sess:
      self.assertAllEqual(expected_lookups, indicator_tensor.eval(session=sess))

  def test_get_sequence_dense_tensor_with_normalizer_fn(self):

    def _increment_two(input_sparse_tensor):
      return sparse_ops.sparse_add(
          input_sparse_tensor,
          sparse_tensor.SparseTensor(((0, 0), (1, 1)), (2.0, 2.0), (2, 2))
      )

    sparse_input = sparse_tensor.SparseTensorValue(
        # example 0, values [[0.], [1]]
        # example 1, [[10.]]
        indices=((0, 0), (0, 1), (1, 0)),
        values=(0., 1., 10.),
        dense_shape=(2, 2))

    # Before _increment_two:
    #   [[0.], [1.]],
    #   [[10.], [0.]],
    # After _increment_two:
    #   [[2.], [1.]],
    #   [[10.], [2.]],
    expected_dense_tensor = [
        [[2.], [1.]],
        [[10.], [2.]],
    ]
    numeric_column = sfc.sequence_numeric_column(
        'aaa', normalizer_fn=_increment_two)

    dense_tensor, _ = numeric_column._get_sequence_dense_tensor(
        _LazyBuilder({'aaa': sparse_input}))

    with monitored_session.MonitoredSession() as sess:
      self.assertAllEqual(
          expected_dense_tensor, dense_tensor.eval(session=sess))

  def test_get_dense_tensor(self):
    # Inputs.
    vocabulary_size = 3
    sparse_input = sparse_tensor.SparseTensorValue(
        # example 0, ids [2]
        # example 1, ids [0, 1]
        # example 2, ids []
        # example 3, ids [1]
        indices=((0, 0), (1, 0), (1, 4), (3, 0)),
        values=(2, 0, 1, 1),
        dense_shape=(4, 5))

    # Embedding variable.
    embedding_dimension = 2
    embedding_values = (
        (1., 2.),  # id 0
        (3., 5.),  # id 1
        (7., 11.)  # 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 = (
        # example 0, ids [2], embedding = [7, 11]
        (7., 11.),
        # example 1, ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
        (2., 3.5),
        # example 2, ids [], embedding = [0, 0]
        (0., 0.),
        # example 3, ids [1], embedding = [3, 5]
        (3., 5.),
    )

    # Build columns.
    categorical_column = fc_lib.categorical_column_with_identity(
        key='aaa', num_buckets=vocabulary_size)
    embedding_column = tpu_fc.embedding_column(
        categorical_column,
        dimension=embedding_dimension,
        initializer=_initializer)

    # Provide sparse input and get dense result.
    embedding_lookup = embedding_column._get_dense_tensor(
        fc._LazyBuilder({
            'aaa': sparse_input
        }))

    # 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]))
    with _initialized_session():
      self.assertAllEqual(embedding_values, global_vars[0].eval())
      self.assertAllEqual(expected_lookups, embedding_lookup.eval())

  def test_sequence_length(self):
    vocabulary_size = 3

    sparse_input_a = sparse_tensor.SparseTensorValue(
        # example 0, ids [2]
        # example 1, ids [0, 1]
        indices=((0, 0), (1, 0), (1, 1)),
        values=(2, 0, 1),
        dense_shape=(2, 2))
    expected_sequence_length_a = [1, 2]
    categorical_column_a = sfc.sequence_categorical_column_with_identity(
        key='aaa', num_buckets=vocabulary_size)

    sparse_input_b = sparse_tensor.SparseTensorValue(
        # example 0, ids [0, 2]
        # example 1, ids [1]
        indices=((0, 0), (0, 1), (1, 0)),
        values=(0, 2, 1),
        dense_shape=(2, 2))
    expected_sequence_length_b = [2, 1]
    categorical_column_b = sfc.sequence_categorical_column_with_identity(
        key='bbb', num_buckets=vocabulary_size)
    shared_embedding_columns = fc.shared_embedding_columns(
        [categorical_column_a, categorical_column_b], dimension=2)

    sequence_length_a = shared_embedding_columns[0]._get_sequence_dense_tensor(
        _LazyBuilder({
            'aaa': sparse_input_a
        }))[1]
    sequence_length_b = shared_embedding_columns[1]._get_sequence_dense_tensor(
        _LazyBuilder({
            'bbb': sparse_input_b
        }))[1]

    with monitored_session.MonitoredSession() as sess:
      sequence_length_a = sess.run(sequence_length_a)
      self.assertAllEqual(expected_sequence_length_a, sequence_length_a)
      self.assertEqual(np.int64, sequence_length_a.dtype)
      sequence_length_b = sess.run(sequence_length_b)
      self.assertAllEqual(expected_sequence_length_b, sequence_length_b)
      self.assertEqual(np.int64, sequence_length_b.dtype)

  def test_sequence_length(self):
    column = sfc.sequence_categorical_column_with_hash_bucket(
        'aaa', hash_bucket_size=10)
    inputs = sparse_tensor.SparseTensorValue(
        indices=((0, 0), (1, 0), (1, 1)),
        values=('omar', 'stringer', 'marlo'),
        dense_shape=(2, 2))
    expected_sequence_length = [1, 2]

    sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs}))

    with monitored_session.MonitoredSession() as sess:
      self.assertAllEqual(
          expected_sequence_length, sequence_length.eval(session=sess))

  def test_sequence_length_with_zeros(self):
    column = sfc.sequence_categorical_column_with_identity(
        'aaa', num_buckets=3)
    inputs = sparse_tensor.SparseTensorValue(
        indices=((1, 0), (3, 0), (3, 1)),
        values=(1, 2, 0),
        dense_shape=(5, 2))
    expected_sequence_length = [0, 1, 0, 2, 0]

    sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs}))

    with monitored_session.MonitoredSession() as sess:
      self.assertAllEqual(
          expected_sequence_length, sequence_length.eval(session=sess))

def _weights(features, weight_column):
  """Fetches weights from features."""
  if weight_column is None:
    return 1.
  if isinstance(weight_column, six.string_types):
    weight_column = feature_column_lib.numeric_column(key=weight_column)
  if not isinstance(weight_column, feature_column_lib._NumericColumn):  # pylint: disable=protected-access
    raise TypeError('Weight column must be either a string or _NumericColumn. '
                    'Given type: {}.'.format(type(weight_column)))
  weights = weight_column._get_dense_tensor(  # pylint: disable=protected-access
      feature_column_lib._LazyBuilder(features))  # pylint: disable=protected-access
  if not (weights.dtype.is_floating or weights.dtype.is_integer):
    raise ValueError('Weight column should be castable to float. '
                     'Given dtype: {}'.format(weights.dtype))
  weights = _maybe_expand_dim(math_ops.to_float(weights, name='weights'))
  return weights

  def test_get_sparse_tensors_inputs3d(self):
    """Tests _get_sparse_tensors when the input is already 3D Tensor."""
    column = sfc.sequence_categorical_column_with_identity(
        'aaa', num_buckets=3)
    inputs = sparse_tensor.SparseTensorValue(
        indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)),
        values=(1, 2, 0),
        dense_shape=(2, 2, 1))

    with self.assertRaisesRegexp(
        errors.InvalidArgumentError,
        r'Column aaa expected ID tensor of rank 2\.\s*'
        r'id_tensor shape:\s*\[2 2 1\]'):
      id_weight_pair = column._get_sparse_tensors(
          _LazyBuilder({'aaa': inputs}))
      with monitored_session.MonitoredSession() as sess:
        id_weight_pair.id_tensor.eval(session=sess)

  def test_sequence_length_with_shape(self):
    """Tests _sequence_length with shape !=(1,)."""
    sparse_input = sparse_tensor.SparseTensorValue(
        # example 0, values [[0.], [1]]
        # example 1, [[10.]]
        indices=((0, 0), (0, 1), (1, 0)),
        values=(0., 1., 10.),
        dense_shape=(2, 2))
    expected_sequence_length = [2, 1]
    numeric_column = sfc.sequence_numeric_column('aaa')

    _, sequence_length = numeric_column._get_sequence_dense_tensor(
        _LazyBuilder({'aaa': sparse_input}))

    with monitored_session.MonitoredSession() as sess:
      self.assertAllEqual(
          expected_sequence_length, sequence_length.eval(session=sess))

  def test_sequence_length(self):
    sparse_input = sparse_tensor.SparseTensorValue(
        # example 0, values [[0., 1., 2.], [3., 4., 5.]]
        # example 1, [[10., 11., 12.]]
        indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5),
                 (1, 0), (1, 1), (1, 2)),
        values=(0., 1., 2., 3., 4., 5., 10., 11., 12.),
        dense_shape=(2, 6))
    expected_sequence_length = [2, 1]
    numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,))

    _, sequence_length = numeric_column._get_sequence_dense_tensor(
        _LazyBuilder({'aaa': sparse_input}))

    with monitored_session.MonitoredSession() as sess:
      self.assertAllEqual(
          expected_sequence_length, sequence_length.eval(session=sess))

  def test_get_sparse_tensors(self):
    column = sfc.sequence_categorical_column_with_identity(
        'aaa', num_buckets=3)
    inputs = sparse_tensor.SparseTensorValue(
        indices=((0, 0), (1, 0), (1, 1)),
        values=(1, 2, 0),
        dense_shape=(2, 2))
    expected_sparse_ids = sparse_tensor.SparseTensorValue(
        indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)),
        values=np.array((1, 2, 0), dtype=np.int64),
        dense_shape=(2, 2, 1))

    id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))

    self.assertIsNone(id_weight_pair.weight_tensor)
    with monitored_session.MonitoredSession() as sess:
      _assert_sparse_tensor_value(
          self,
          expected_sparse_ids,
          id_weight_pair.id_tensor.eval(session=sess))

  def test_get_sequence_dense_tensor_with_shape(self):
    """Tests get_sequence_dense_tensor with shape !=(1,)."""
    sparse_input = sparse_tensor.SparseTensorValue(
        # example 0, values [[0., 1., 2.], [3., 4., 5.]]
        # example 1, [[10., 11., 12.]]
        indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5),
                 (1, 0), (1, 1), (1, 2)),
        values=(0., 1., 2., 3., 4., 5., 10., 11., 12.),
        dense_shape=(2, 6))
    expected_dense_tensor = [
        [[0., 1., 2.], [3., 4., 5.]],
        [[10., 11., 12.], [0., 0., 0.]],
    ]
    numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,))

    dense_tensor, _ = numeric_column._get_sequence_dense_tensor(
        _LazyBuilder({'aaa': sparse_input}))

    with monitored_session.MonitoredSession() as sess:
      self.assertAllEqual(
          expected_dense_tensor, dense_tensor.eval(session=sess))

  def test_get_dense_tensor_multi_dim(self):
    """Tests get_sequence_dense_tensor for multi-dim numeric_column."""
    sparse_input = sparse_tensor.SparseTensorValue(
        # example 0, values [[[0., 1.],  [2., 3.]], [[4., 5.],  [6., 7.]]]
        # example 1, [[[10., 11.],  [12., 13.]]]
        indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7),
                 (1, 0), (1, 1), (1, 2), (1, 3)),
        values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.),
        dense_shape=(2, 8))
    expected_dense_tensor = [
        [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]],
        [[[10., 11.], [12., 13.]], [[0., 0.], [0., 0.]]],
    ]
    numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2))

    dense_tensor, _ = numeric_column._get_sequence_dense_tensor(
        _LazyBuilder({'aaa': sparse_input}))

    with monitored_session.MonitoredSession() as sess:
      self.assertAllEqual(
          expected_dense_tensor, dense_tensor.eval(session=sess))

def sequence_input_layer(
    features,
    feature_columns,
    weight_collections=None,
    trainable=True):
  """"Builds input layer for sequence input.

  All `feature_columns` must be sequence dense columns with the same
  `sequence_length`. The output of this method can be fed into sequence
  networks, such as RNN.

  The output of this method is a 3D `Tensor` of shape `[batch_size, T, D]`.
  `T` is the maximum sequence length for this batch, which could differ from
  batch to batch.

  If multiple `feature_columns` are given with `Di` `num_elements` each, their
  outputs are concatenated. So, the final `Tensor` has shape
  `[batch_size, T, D0 + D1 + ... + Dn]`.

  Example:

  ```python
  rating = sequence_numeric_column('rating')
  watches = sequence_categorical_column_with_identity(
      'watches', num_buckets=1000)
  watches_embedding = embedding_column(watches, dimension=10)
  columns = [rating, watches]

  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:
    features: A dict mapping keys to tensors.
    feature_columns: An iterable of dense sequence columns. Valid columns are
      - `embedding_column` that wraps a `sequence_categorical_column_with_*`
      - `sequence_numeric_column`.
    weight_collections: A list of collection names to which the Variable will be
      added. Note that variables will also be added to collections
      `tf.GraphKeys.GLOBAL_VARIABLES` and `ops.GraphKeys.MODEL_VARIABLES`.
    trainable: If `True` also add the variable to the graph collection
      `GraphKeys.TRAINABLE_VARIABLES`.

  Returns:
    An `(input_layer, sequence_length)` tuple where:
    - input_layer: A float `Tensor` of shape `[batch_size, T, D]`.
        `T` is the maximum sequence length for this batch, which could differ
        from batch to batch. `D` is the sum of `num_elements` for all
        `feature_columns`.
    - sequence_length: An int `Tensor` of shape `[batch_size]`. The sequence
        length for each example.

  Raises:
    ValueError: If any of the `feature_columns` is the wrong type.
  """
  feature_columns = fc._normalize_feature_columns(feature_columns)
  for c in feature_columns:
    if not isinstance(c, fc._SequenceDenseColumn):
      raise ValueError(
          'All feature_columns must be of type _SequenceDenseColumn. '
          'You can wrap a sequence_categorical_column with an embedding_column '
          'or indicator_column. '
          'Given (type {}): {}'.format(type(c), c))

  with variable_scope.variable_scope(
      None, default_name='sequence_input_layer', values=features.values()):
    builder = fc._LazyBuilder(features)
    output_tensors = []
    sequence_lengths = []
    ordered_columns = []

    for column in sorted(feature_columns, key=lambda x: x.name):
      ordered_columns.append(column)
      with variable_scope.variable_scope(
          None, default_name=column._var_scope_name):
        dense_tensor, sequence_length = column._get_sequence_dense_tensor(
            builder,
            weight_collections=weight_collections,
            trainable=trainable)
        # Flattens the final dimension to produce a 3D Tensor.
        num_elements = column._variable_shape.num_elements()
        shape = array_ops.shape(dense_tensor)
        target_shape = [shape[0], shape[1], num_elements]
        output_tensors.append(
            array_ops.reshape(dense_tensor, shape=target_shape))
        sequence_lengths.append(sequence_length)

    fc._verify_static_batch_size_equality(output_tensors, ordered_columns)
    fc._verify_static_batch_size_equality(sequence_lengths, ordered_columns)
    sequence_length = _assert_all_equal_and_return(sequence_lengths)

    return array_ops.concat(output_tensors, -1), sequence_length

def _get_weights_and_check_match_logits(
    features, weight_column, logits, allow_per_logit_weights=False):
  """Fetches weights from features and checks that the shape matches logits.

  Consider logits of shape [D0, D1, ... DN, logits_dimension]. Weights shape
  can be either:
  * [D0, D1, ... DN, logits_dimension] if `allow_per_logit_weights=True`.
  * [D0, D1, ... DN, 1]
  * [D0, D1, ... DN]: In this case, weights is reshaped into
    [D0, D1, ... DN, 1] to work with weight broadcasting rules.

  Args:
    features: The features dict that contains weights.
    weight_column: The weight column. If not given, this method returns 1.
    logits: logits Tensor.
    allow_per_logit_weights: Boolean. Whether we allow weights along the logits
      dimension, namely shape `[D0, D1, ... DN, logits_dimension]`.
  Returns:
    Validated and reshaped weights Tensor.
  Raises:
    ValueError: If the weights `Tensor` cannot be cast into float.
  """
  if allow_per_logit_weights:
    err_msg = (
        'weights shape must be [D0, D1, ... DN], [D0, D1, ... DN, 1] or '
        '[D0, D1, ... DN, logits_dimension]')
  else:
    err_msg = (
        'weights shape must be [D0, D1, ... DN] or [D0, D1, ... DN, 1]')
  with ops.name_scope(
      None, 'weights',
      values=tuple(six.itervalues(features)) + (logits,)) as scope:
    # Fetch the weights.
    if weight_column is None:
      return 1.
    if isinstance(weight_column, six.string_types):
      weight_column = feature_column_lib.numeric_column(
          key=weight_column, shape=(1,))
    if not isinstance(weight_column, feature_column_lib._NumericColumn):  # pylint: disable=protected-access
      raise TypeError('Weight column must be either a string or _NumericColumn.'
                      ' Given type: {}.'.format(type(weight_column)))
    weights = weight_column._get_dense_tensor(  # pylint: disable=protected-access
        feature_column_lib._LazyBuilder(features))  # pylint: disable=protected-access
    if not (weights.dtype.is_floating or weights.dtype.is_integer):
      raise ValueError('Weight column should be castable to float. '
                       'Given dtype: {}'.format(weights.dtype))
    weights = math_ops.to_float(weights, name='weights')

    # Validate the weights shape.
    weights_shape = array_ops.shape(weights, name='weights_shape')
    logits_shape = array_ops.shape(logits, name='logits_shape')
    if (weights.shape.ndims is not None and logits.shape.ndims is not None and
        weights.shape.ndims == logits.shape.ndims - 1):
      assert_dimension = check_ops.assert_equal(
          logits_shape[:-1], weights_shape, message=err_msg,
          data=['logits_shape: ', logits_shape,
                'weights_shape: ', weights_shape])
      with ops.control_dependencies([assert_dimension]):
        return array_ops.expand_dims(weights, -1, name=scope)
    supported_weights_shape = array_ops.concat([logits_shape[:-1], [1]], axis=0)
    if allow_per_logit_weights:
      condition = math_ops.reduce_any(
          [math_ops.reduce_all(math_ops.equal(logits_shape, weights_shape)),
           math_ops.reduce_all(math_ops.equal(
               supported_weights_shape, weights_shape))])
      assert_dimension = control_flow_ops.Assert(
          condition=condition,
          data=[err_msg, 'logits_shape: ', logits_shape,
                'weights_shape: ', weights_shape])
    else:
      assert_dimension = check_ops.assert_equal(
          supported_weights_shape, weights_shape, message=err_msg,
          data=['logits_shape: ', logits_shape,
                'weights_shape: ', weights_shape])
    with ops.control_dependencies([assert_dimension]):
      return array_ops.identity(weights, name=scope)

def sequence_input_layer(
    features,
    feature_columns,
    weight_collections=None,
    trainable=True,
    scope=None):
  """"Builds input layer for sequence input.

  All `feature_columns` must be sequence dense columns with the same
  `sequence_length`. The output of this method can be fed into sequence
  networks, such as RNN.

  The output of this method is a 3D `Tensor` of shape `[batch_size, T, D]`.
  `T` is the maximum sequence length for this batch, which could differ from
  batch to batch.

  If multiple `feature_columns` are given with `Di` `num_elements` each, their
  outputs are concatenated. So, the final `Tensor` has shape
  `[batch_size, T, D0 + D1 + ... + Dn]`.

  Example:

  ```python
  rating = sequence_numeric_column('rating')
  watches = sequence_categorical_column_with_identity(
      'watches', num_buckets=1000)
  watches_embedding = embedding_column(watches, dimension=10)
  columns = [rating, watches]

  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)
  ```

  Returns:
    An `(input_layer, sequence_length)` tuple where:
    - input_layer: A float `Tensor` of shape `[batch_size, T, D]`.
        `T` is the maximum sequence length for this batch, which could differ
        from batch to batch. `D` is the sum of `num_elements` for all
        `feature_columns`.
    - sequence_length: An int `Tensor` of shape `[batch_size]`. The sequence
        length for each example.
  Raises:
    ValueError: If any of the `feature_columns` is the wrong type.
  """
  feature_columns = fc._clean_feature_columns(feature_columns)
  for c in feature_columns:
    if not isinstance(c, _SequenceDenseColumn):
      raise ValueError(
          'All feature_columns must be of type _SequenceDenseColumn. '
          'Given (type {}): {}'.format(type(c), c))

  with variable_scope.variable_scope(
      scope, default_name='sequence_input_layer', values=features.values()):
    builder = fc._LazyBuilder(features)
    output_tensors = []
    sequence_lengths = []
    ordered_columns = []
    for column in sorted(feature_columns, key=lambda x: x.name):
      ordered_columns.append(column)
      with variable_scope.variable_scope(
          None, default_name=column._var_scope_name):
        dense_tensor, sequence_length = column._get_sequence_dense_tensor(
            builder,
            weight_collections=weight_collections,
            trainable=trainable)
        # Flattens the final dimension to produce a 3D Tensor.
        num_elements = column._variable_shape.num_elements()
        shape = array_ops.shape(dense_tensor)
        output_tensors.append(
            array_ops.reshape(
                dense_tensor,
                shape=array_ops.concat([shape[:2], [num_elements]], axis=0)))
        sequence_lengths.append(sequence_length)
    fc._verify_static_batch_size_equality(output_tensors, ordered_columns)
    # TODO(b/73160931): Verify sequence_length equality.
    return array_ops.concat(output_tensors, -1), sequence_lengths[0]