# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# ==============================================================================
"""This API defines FeatureColumn abstraction.

FeatureColumns provide a high level abstraction for ingesting and representing
features. FeatureColumns are also the primary way of encoding features for
canned `tf.estimator.Estimator`s.

When using FeatureColumns with `Estimators`, the type of feature column you
should choose depends on (1) the feature type and (2) the model type.

1. Feature type:

  * Continuous features can be represented by `numeric_column`.
  * Categorical features can be represented by any `categorical_column_with_*`
  column:
    - `categorical_column_with_vocabulary_list`
    - `categorical_column_with_vocabulary_file`
    - `categorical_column_with_hash_bucket`
    - `categorical_column_with_identity`
    - `weighted_categorical_column`

2. Model type:

  * Deep neural network models (`DNNClassifier`, `DNNRegressor`).

    Continuous features can be directly fed into deep neural network models.

      age_column = numeric_column("age")

    To feed sparse features into DNN models, wrap the column with
    `embedding_column` or `indicator_column`. `indicator_column` is recommended
    for features with only a few possible values. For features with many
    possible values, to reduce the size of your model, `embedding_column` is
    recommended.

      embedded_dept_column = embedding_column(
          categorical_column_with_vocabulary_list(
              "department", ["math", "philosophy", ...]), dimension=10)

  * Wide (aka linear) models (`LinearClassifier`, `LinearRegressor`).

    Sparse features can be fed directly into linear models. They behave like an
    indicator column but with an efficient implementation.

      dept_column = categorical_column_with_vocabulary_list("department",
          ["math", "philosophy", "english"])

    It is recommended that continuous features be bucketized before being
    fed into linear models.

      bucketized_age_column = bucketized_column(
          source_column=age_column,
          boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])

    Sparse features can be crossed (also known as conjuncted or combined) in
    order to form non-linearities, and then fed into linear models.

      cross_dept_age_column = crossed_column(
          columns=["department", bucketized_age_column],
          hash_bucket_size=1000)

Example of building canned `Estimator`s using FeatureColumns:

  ```python
  # Define features and transformations
  deep_feature_columns = [age_column, embedded_dept_column]
  wide_feature_columns = [dept_column, bucketized_age_column,
      cross_dept_age_column]

  # Build deep model
  estimator = DNNClassifier(
      feature_columns=deep_feature_columns,
      hidden_units=[500, 250, 50])
  estimator.train(...)

  # Or build a wide model
  estimator = LinearClassifier(
      feature_columns=wide_feature_columns)
  estimator.train(...)

  # Or build a wide and deep model!
  estimator = DNNLinearCombinedClassifier(
      linear_feature_columns=wide_feature_columns,
      dnn_feature_columns=deep_feature_columns,
      dnn_hidden_units=[500, 250, 50])
  estimator.train(...)
  ```


FeatureColumns can also be transformed into a generic input layer for
custom models using `input_layer`.

Example of building model using FeatureColumns, this can be used in a
`model_fn` which is given to the {tf.estimator.Estimator}:

  ```python
  # Building model via layers

  deep_feature_columns = [age_column, embedded_dept_column]
  columns_to_tensor = parse_feature_columns_from_examples(
      serialized=my_data,
      feature_columns=deep_feature_columns)
  first_layer = input_layer(
      features=columns_to_tensor,
      feature_columns=deep_feature_columns)
  second_layer = fully_connected(first_layer, ...)
  ```

NOTE: Functions prefixed with "_" indicate experimental or private parts of
the API subject to change, and should not be relied upon!
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import abc
import collections
import math

import numpy as np
import six


from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
from tensorflow.python.framework import tensor_shape
from tensorflow.python.keras.engine.base_layer import Layer
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import lookup_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import parsing_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import string_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import gfile
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training import checkpoint_utils
from tensorflow.python.util import nest


class StateManager(object):
  """Manages the state associated with FeatureColumns.

  Some `FeatureColumn`s create variables or resources to assist their
  computation. The `StateManager` is responsible for creating and storing these
  objects since `FeatureColumn`s are supposed to be stateless configuration
  only.
  """

  def create_variable(self,
                      feature_column,
                      name,
                      shape,
                      dtype=None,
                      trainable=True,
                      initializer=None):
    """Creates a new variable.

    Args:
      feature_column: A `FeatureColumn` object this variable corresponds to.
      name: variable name.
      shape: variable shape.
      dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
      trainable: Whether this variable is trainable or not.
      initializer: initializer instance (callable).

    Returns:
      The created variable.
    """
    del feature_column, name, shape, dtype, trainable, initializer
    raise NotImplementedError('StateManager.create_variable')

  def add_variable(self, feature_column, var):
    """Adds an existing variable to the state.

    Args:
      feature_column: A `FeatureColumn` object to associate this variable with.
      var: The variable.
    """
    del feature_column, var
    raise NotImplementedError('StateManager.add_variable')

  def get_variable(self, feature_column, name):
    """Returns an existing variable.

    Args:
      feature_column: A `FeatureColumn` object this variable corresponds to.
      name: variable name.
    """
    del feature_column, name
    raise NotImplementedError('StateManager.get_var')

  def add_resource(self, feature_column, name, resource):
    """Creates a new resource.

    Resources can be things such as tables etc.

    Args:
      feature_column: A `FeatureColumn` object this resource corresponds to.
      name: Name of the resource.
      resource: The resource.

    Returns:
      The created resource.
    """
    del feature_column, name, resource
    raise NotImplementedError('StateManager.add_resource')

  def get_resource(self, feature_column, name):
    """Returns an already created resource.

    Resources can be things such as tables etc.

    Args:
      feature_column: A `FeatureColumn` object this variable corresponds to.
      name: Name of the resource.
    """
    del feature_column, name
    raise NotImplementedError('StateManager.get_resource')


class _StateManagerImpl(StateManager):
  """Manages the state of FeatureLayer and LinearModel."""

  def __init__(self, layer, trainable):
    """Creates an _StateManagerImpl object.

    Args:
      layer: The input layer this state manager is associated with.
      trainable: Whether by default, variables created are trainable or not.
    """
    self._trainable = trainable
    self._layer = layer
    self._cols_to_vars_map = collections.defaultdict(lambda: {})

  def create_variable(self,
                      feature_column,
                      name,
                      shape,
                      dtype=None,
                      trainable=True,
                      initializer=None):
    if name in self._cols_to_vars_map[feature_column]:
      raise ValueError('Variable already exists.')

    var = self._layer.add_variable(
        name=name,
        shape=shape,
        dtype=dtype,
        initializer=initializer,
        trainable=self._trainable and trainable,
        use_resource=True,
        # TODO(rohanj): Get rid of this hack once we have a mechanism for
        # specifying a default partitioner for an entire layer. In that case,
        # the default getter for Layers should work.
        getter=variable_scope.get_variable)
    self._cols_to_vars_map[feature_column][name] = var
    return var

  def get_variable(self, feature_column, name):
    if name in self._cols_to_vars_map[feature_column]:
      return self._cols_to_vars_map[feature_column][name]
    raise ValueError('Variable does not exist.')


class FeatureLayer(Layer):
  """A layer that produces a dense `Tensor` based on given `feature_columns`.

  Generally a single example in training data is described with FeatureColumns.
  At the first layer of the model, this column oriented data should be converted
  to a single `Tensor`.

  This layer can be called multiple times with different features.

  Example:

  ```python
  price = numeric_column('price')
  keywords_embedded = embedding_column(
      categorical_column_with_hash_bucket("keywords", 10K), dimensions=16)
  columns = [price, keywords_embedded, ...]
  feature_layer = FeatureLayer(columns)

  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  dense_tensor = feature_layer(features)
  for units in [128, 64, 32]:
    dense_tensor = tf.layers.dense(dense_tensor, units, tf.nn.relu)
  prediction = tf.layers.dense(dense_tensor, 1).
  ```
  """

  def __init__(self,
               feature_columns,
               trainable=True,
               name=None,
               shared_state_manager=None,
               **kwargs):
    """Constructs a FeatureLayer.

    Args:
      feature_columns: An iterable containing the FeatureColumns to use as
        inputs to your model. All items should be instances of classes derived
        from `DenseColumn` such as `numeric_column`, `embedding_column`,
        `bucketized_column`, `indicator_column`. If you have categorical
        features, you can wrap them with an `embedding_column` or
        `indicator_column`.
      trainable: If `True` also add the variable to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
      name: Name to give to the FeatureLayer.
      shared_state_manager: SharedEmbeddingStateManager that manages the state
        of SharedEmbeddingColumns. The state of SharedEmbeddingColumns, unlike
        regular embedding columns cannot be owned by the InputLayer itself since
        SharedEmbeddingColumns can be shared across different InputLayers. As a
        result users are expected to create a SharedEmbeddingStateManager object
        which would be responsible for managing the shared state and can be
        passed into different InputLayer objects to share state. For example,

        ```python
        sc_1, sc_2 = shared_embedding_column_v2(...)
        sc_3, sc_4 = shared_embedding_column_v2(...)
        ssm = SharedEmbeddingStateManager()
        feature_layer1 = FeatureLayer([sc_1, sc_3], ...,
                                      shared_state_manager=ssm)
        feature_layer2 = FeatureLayer([sc_2, sc_4], ...,
                                      shared_state_manager=ssm)
        ```
        now input_layer1 and input_layer2 will share variables across. If
        sharing is not desired, one can create 2 separate
        SharedEmbeddingStateManager objects

        ```python
        ssm1 = SharedEmbeddingStateManager()
        ssm2 = SharedEmbeddingStateManager()
        feature_layer1 = FeatureLayer([sc_1, sc_3], ...,
                                      shared_state_manager=ssm1)
        feature_layer2 = FeatureLayer([sc_2, sc_4], ...,
                                      shared_state_manager=ssm2)
        ```
      **kwargs: Keyword arguments to construct a layer.

    Raises:
      ValueError: if an item in `feature_columns` is not a `DenseColumn`.
    """
    super(FeatureLayer, self).__init__(name=name, trainable=trainable, **kwargs)

    self._feature_columns = _normalize_feature_columns(feature_columns)
    self._state_manager = _StateManagerImpl(self, self.trainable)
    self._shared_state_manager = shared_state_manager
    for column in sorted(self._feature_columns, key=lambda x: x.name):
      if not isinstance(column, DenseColumn):
        raise ValueError(
            'Items of feature_columns must be a DenseColumn. '
            'You can wrap a categorical column with an '
            'embedding_column or indicator_column. Given: {}'.format(column))

  @property
  def _is_feature_layer(self):
    return True

  def build(self, _):
    for column in sorted(self._feature_columns, key=lambda x: x.name):
      if isinstance(column, SharedEmbeddingColumn):
        column.create_state(self._shared_state_manager)
      else:
        with variable_scope._pure_variable_scope(self.name):  # pylint: disable=protected-access
          with variable_scope._pure_variable_scope(column.name):  # pylint: disable=protected-access
            column.create_state(self._state_manager)
      super(FeatureLayer, self).build(None)

  def call(self, features, cols_to_output_tensors=None):
    """Returns a dense tensor corresponding to the `feature_columns`.

    Args:
      features: A mapping from key to tensors. `FeatureColumn`s look up via
        these keys. For example `numeric_column('price')` will look at 'price'
        key in this dict. Values can be a `SparseTensor` or a `Tensor` depends
        on corresponding `FeatureColumn`.
      cols_to_output_tensors: If not `None`, this will be filled with a dict
        mapping feature columns to output tensors created.

    Returns:
      A `Tensor` which represents input layer of a model. Its shape
      is (batch_size, first_layer_dimension) and its dtype is `float32`.
      first_layer_dimension is determined based on given `feature_columns`.

    Raises:
      ValueError: If features are not a dictionary.
    """
    if not isinstance(features, dict):
      raise ValueError('We expected a dictionary here. Instead we got: ',
                       features)
    transformation_cache = FeatureTransformationCache(features)
    output_tensors = []
    ordered_columns = []
    for column in sorted(self._feature_columns, key=lambda x: x.name):
      with ops.name_scope(column.name):
        ordered_columns.append(column)
        if isinstance(column, SharedEmbeddingColumn):
          tensor = column.get_dense_tensor(transformation_cache,
                                           self._shared_state_manager)
        else:
          tensor = column.get_dense_tensor(transformation_cache,
                                           self._state_manager)
        num_elements = column.variable_shape.num_elements()
        batch_size = array_ops.shape(tensor)[0]
        tensor = array_ops.reshape(tensor, shape=(batch_size, num_elements))
        output_tensors.append(tensor)
        if cols_to_output_tensors is not None:
          cols_to_output_tensors[column] = tensor

    _verify_static_batch_size_equality(output_tensors, ordered_columns)
    return array_ops.concat(output_tensors, 1)

  def compute_output_shape(self, input_shape):
    total_elements = 0
    for column in sorted(self._feature_columns, key=lambda x: x.name):
      total_elements += column.variable_shape.num_elements()
    return (input_shape[0], total_elements)


def _strip_leading_slashes(name):
  return name.rsplit('/', 1)[-1]


class LinearModel(Layer):
  """Produces a linear prediction `Tensor` based on given `feature_columns`.

  This layer generates a weighted sum based on output dimension `units`.
  Weighted sum refers to logits in classification problems. It refers to the
  prediction itself for linear regression problems.

  Note on supported columns: `LinearModel` treats categorical columns as
  `indicator_column`s. To be specific, assume the input as `SparseTensor` looks
  like:

  ```python
    shape = [2, 2]
    {
        [0, 0]: "a"
        [1, 0]: "b"
        [1, 1]: "c"
    }
  ```
  `linear_model` assigns weights for the presence of "a", "b", "c' implicitly,
  just like `indicator_column`, while `input_layer` explicitly requires wrapping
  each of categorical columns with an `embedding_column` or an
  `indicator_column`.

  Example of usage:

  ```python
  price = numeric_column('price')
  price_buckets = bucketized_column(price, boundaries=[0., 10., 100., 1000.])
  keywords = categorical_column_with_hash_bucket("keywords", 10K)
  keywords_price = crossed_column('keywords', price_buckets, ...)
  columns = [price_buckets, keywords, keywords_price ...]
  linear_model = LinearModel(columns)

  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  prediction = linear_model(features)
  ```
  """

  def __init__(self,
               feature_columns,
               units=1,
               sparse_combiner='sum',
               trainable=True,
               name=None,
               shared_state_manager=None,
               **kwargs):
    """Constructs a LinearModel.

    Args:
      feature_columns: An iterable containing the FeatureColumns to use as
        inputs to your model. All items should be instances of classes derived
        from `_FeatureColumn`s.
      units: An integer, dimensionality of the output space. Default value is 1.
      sparse_combiner: A string specifying how to reduce if a categorical column
        is multivalent. Except `numeric_column`, almost all columns passed to
        `linear_model` are considered as categorical columns.  It combines each
        categorical column independently. Currently "mean", "sqrtn" and "sum"
        are supported, with "sum" the default for linear model. "sqrtn" often
        achieves good accuracy, in particular with bag-of-words columns.
          * "sum": do not normalize features in the column
          * "mean": do l1 normalization on features in the column
          * "sqrtn": do l2 normalization on features in the column
        For example, for two features represented as the categorical columns:

          ```python
          # Feature 1

          shape = [2, 2]
          {
              [0, 0]: "a"
              [0, 1]: "b"
              [1, 0]: "c"
          }

          # Feature 2

          shape = [2, 3]
          {
              [0, 0]: "d"
              [1, 0]: "e"
              [1, 1]: "f"
              [1, 2]: "g"
          }
          ```

        with `sparse_combiner` as "mean", the linear model outputs conceptly are
        ```
        y_0 = 1.0 / 2.0 * ( w_a + w_ b) + w_c + b_0
        y_1 = w_d + 1.0 / 3.0 * ( w_e + w_ f + w_g) + b_1
        ```
        where `y_i` is the output, `b_i` is the bias, and `w_x` is the weight
        assigned to the presence of `x` in the input features.
      trainable: If `True` also add the variable to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
      name: Name to give to the Linear Model. All variables and ops created will
        be scoped by this name.
      shared_state_manager: SharedEmbeddingStateManager that manages the state
        of SharedEmbeddingColumns. For more info, look at `FeatureLayer`.
      **kwargs: Keyword arguments to construct a layer.

    Raises:
      ValueError: if an item in `feature_columns` is neither a `DenseColumn`
        nor `CategoricalColumn`.
    """
    super(LinearModel, self).__init__(name=name, trainable=trainable, **kwargs)

    self._feature_columns = _normalize_feature_columns(feature_columns)
    self._feature_columns = sorted(self._feature_columns, key=lambda x: x.name)
    for column in self._feature_columns:
      if not isinstance(column, (DenseColumn, CategoricalColumn)):
        raise ValueError(
            'Items of feature_columns must be either a '
            'DenseColumn or CategoricalColumn. Given: {}'.format(column))

    self._units = units
    self._sparse_combiner = sparse_combiner

    self._state_manager = _StateManagerImpl(self, self.trainable)
    self._shared_state_manager = shared_state_manager
    self._bias_variable = None

  def build(self, _):
    # Create state for shared embedding columns.
    for column in self._feature_columns:
      if isinstance(column, SharedEmbeddingColumn):
        column.create_state(self._shared_state_manager)

    # We need variable scopes for now because we want the variable partitioning
    # information to percolate down. We also use _pure_variable_scope's here
    # since we want to open up a name_scope in the `call` method while creating
    # the ops.
    with variable_scope._pure_variable_scope(self.name):  # pylint: disable=protected-access
      for column in self._feature_columns:
        with variable_scope._pure_variable_scope(column.name):  # pylint: disable=protected-access
          # Create the state for each feature column
          if not isinstance(column, SharedEmbeddingColumn):
            column.create_state(self._state_manager)

          # Create a weight variable for each column.
          if isinstance(column, CategoricalColumn):
            first_dim = column.num_buckets
          else:
            first_dim = column.variable_shape.num_elements()
          self._state_manager.create_variable(
              column,
              name='weights',
              dtype=dtypes.float32,
              shape=(first_dim, self._units),
              initializer=init_ops.zeros_initializer(),
              trainable=self.trainable)

      # Create a bias variable.
      self._bias_variable = self.add_variable(
          name='bias_weights',
          dtype=dtypes.float32,
          shape=[self._units],
          initializer=init_ops.zeros_initializer(),
          trainable=self.trainable,
          use_resource=True,
          # TODO(rohanj): Get rid of this hack once we have a mechanism for
          # specifying a default partitioner for an entire layer. In that case,
          # the default getter for Layers should work.
          getter=variable_scope.get_variable)

    super(LinearModel, self).build(None)

  def call(self, features):
    """Returns a `Tensor` the represents the predictions of a linear model.

    Args:
      features: A mapping from key to tensors. `_FeatureColumn`s look up via
        these keys. For example `numeric_column('price')` will look at 'price'
        key in this dict. Values are `Tensor` or `SparseTensor` depending on
        corresponding `_FeatureColumn`.

    Returns:
      A `Tensor` which represents predictions/logits of a linear model. Its
      shape is (batch_size, units) and its dtype is `float32`.

    Raises:
      ValueError: If features are not a dictionary.
    """
    if not isinstance(features, dict):
      raise ValueError('We expected a dictionary here. Instead we got: ',
                       features)
    with ops.name_scope(self.name):
      transformation_cache = FeatureTransformationCache(features)
      weighted_sums = []
      for column in self._feature_columns:
        with ops.name_scope(column.name):
          # All the weights used in the linear model are owned by the state
          # manager associated with this Linear Model.
          weight_var = self._state_manager.get_variable(column, 'weights')

          # The embedding weights for the SharedEmbeddingColumn are owned by
          # the shared_state_manager and so we need to pass that in while
          # creating the weighted sum. For all other columns, the state is owned
          # by the Linear Model's state manager.
          if isinstance(column, SharedEmbeddingColumn):
            state_manager = self._shared_state_manager
          else:
            state_manager = self._state_manager
          weighted_sum = _create_weighted_sum(
              column=column,
              transformation_cache=transformation_cache,
              state_manager=state_manager,
              sparse_combiner=self._sparse_combiner,
              weight_var=weight_var)
          weighted_sums.append(weighted_sum)

      _verify_static_batch_size_equality(weighted_sums, self._feature_columns)
      predictions_no_bias = math_ops.add_n(
          weighted_sums, name='weighted_sum_no_bias')
      predictions = nn_ops.bias_add(
          predictions_no_bias, self._bias_variable, name='weighted_sum')
      return predictions

  @property
  def bias_variable(self):
    return self._bias_variable


def _transform_features(features, feature_columns, state_manager):
  """Returns transformed features based on features columns passed in.

  Please note that most probably you would not need to use this function. Please
  check `input_layer` and `linear_model` to see whether they will
  satisfy your use case or not.

  Example:

  ```python
  # Define features and transformations
  crosses_a_x_b = crossed_column(
      columns=["sparse_feature_a", "sparse_feature_b"], hash_bucket_size=10000)
  price_buckets = bucketized_column(
      source_column=numeric_column("price"), boundaries=[...])

  columns = [crosses_a_x_b, price_buckets]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  transformed = transform_features(features=features, feature_columns=columns)

  assertCountEqual(columns, transformed.keys())
  ```

  Args:
    features: A mapping from key to tensors. `FeatureColumn`s look up via these
      keys. For example `numeric_column('price')` will look at 'price' key in
      this dict. Values can be a `SparseTensor` or a `Tensor` depends on
      corresponding `FeatureColumn`.
    feature_columns: An iterable containing all the `FeatureColumn`s.
    state_manager: A StateManager object that holds the FeatureColumn state.

  Returns:
    A `dict` mapping `FeatureColumn` to `Tensor` and `SparseTensor` values.
  """
  feature_columns = _normalize_feature_columns(feature_columns)
  outputs = {}
  with ops.name_scope(
      None, default_name='transform_features', values=features.values()):
    transformation_cache = FeatureTransformationCache(features)
    for column in sorted(feature_columns, key=lambda x: x.name):
      with ops.name_scope(None, default_name=column.name):
        outputs[column] = transformation_cache.get(column, state_manager)
  return outputs


def make_parse_example_spec(feature_columns):
  """Creates parsing spec dictionary from input feature_columns.

  The returned dictionary can be used as arg 'features' in `tf.parse_example`.

  Typical usage example:

  ```python
  # Define features and transformations
  feature_a = categorical_column_with_vocabulary_file(...)
  feature_b = numeric_column(...)
  feature_c_bucketized = bucketized_column(numeric_column("feature_c"), ...)
  feature_a_x_feature_c = crossed_column(
      columns=["feature_a", feature_c_bucketized], ...)

  feature_columns = set(
      [feature_b, feature_c_bucketized, feature_a_x_feature_c])
  features = tf.parse_example(
      serialized=serialized_examples,
      features=make_parse_example_spec(feature_columns))
  ```

  For the above example, make_parse_example_spec would return the dict:

  ```python
  {
      "feature_a": parsing_ops.VarLenFeature(tf.string),
      "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
      "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
  }
  ```

  Args:
    feature_columns: An iterable containing all feature columns. All items
      should be instances of classes derived from `FeatureColumn`.

  Returns:
    A dict mapping each feature key to a `FixedLenFeature` or `VarLenFeature`
    value.

  Raises:
    ValueError: If any of the given `feature_columns` is not a `FeatureColumn`
      instance.
  """
  result = {}
  for column in feature_columns:
    if not isinstance(column, FeatureColumn):
      raise ValueError('All feature_columns must be FeatureColumn instances. '
                       'Given: {}'.format(column))
    config = column.parse_example_spec
    for key, value in six.iteritems(config):
      if key in result and value != result[key]:
        raise ValueError(
            'feature_columns contain different parse_spec for key '
            '{}. Given {} and {}'.format(key, value, result[key]))
    result.update(config)
  return result


def embedding_column(
    categorical_column, dimension, combiner='mean', initializer=None,
    ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None,
    trainable=True):
  """`_DenseColumn` that converts from sparse, categorical input.

  Use this when your inputs are sparse, but you want to convert them to a dense
  representation (e.g., to feed to a DNN).

  Inputs must be a `_CategoricalColumn` created by any of the
  `categorical_column_*` function. Here is an example of using
  `embedding_column` with `DNNClassifier`:

  ```python
  video_id = categorical_column_with_identity(
      key='video_id', num_buckets=1000000, default_value=0)
  columns = [embedding_column(video_id, 9),...]

  estimator = tf.estimator.DNNClassifier(feature_columns=columns, ...)

  label_column = ...
  def input_fn():
    features = tf.parse_example(
        ..., features=make_parse_example_spec(columns + [label_column]))
    labels = features.pop(label_column.name)
    return features, labels

  estimator.train(input_fn=input_fn, steps=100)
  ```

  Here is an example using `embedding_column` with model_fn:

  ```python
  def model_fn(features, ...):
    video_id = categorical_column_with_identity(
        key='video_id', num_buckets=1000000, default_value=0)
    columns = [embedding_column(video_id, 9),...]
    dense_tensor = input_layer(features, columns)
    # Form DNN layers, calculate loss, and return EstimatorSpec.
    ...
  ```

  Args:
    categorical_column: A `_CategoricalColumn` created by a
      `categorical_column_with_*` function. This column produces the sparse IDs
      that are inputs to the embedding lookup.
    dimension: An integer specifying dimension of the embedding, must be > 0.
    combiner: A string specifying how to reduce if there are multiple entries
      in a single row. Currently 'mean', 'sqrtn' and 'sum' are supported, with
      'mean' the default. 'sqrtn' often achieves good accuracy, in particular
      with bag-of-words columns. Each of this can be thought as example level
      normalizations on the column. For more information, see
      `tf.embedding_lookup_sparse`.
    initializer: A variable initializer function to be used in embedding
      variable initialization. If not specified, defaults to
      `tf.truncated_normal_initializer` with mean `0.0` and standard deviation
      `1/sqrt(dimension)`.
    ckpt_to_load_from: String representing checkpoint name/pattern from which to
      restore column weights. Required if `tensor_name_in_ckpt` is not `None`.
    tensor_name_in_ckpt: Name of the `Tensor` in `ckpt_to_load_from` from
      which to restore the column weights. Required if `ckpt_to_load_from` is
      not `None`.
    max_norm: If not `None`, embedding values are l2-normalized to this value.
    trainable: Whether or not the embedding is trainable. Default is True.

  Returns:
    `_DenseColumn` that converts from sparse input.

  Raises:
    ValueError: if `dimension` not > 0.
    ValueError: if exactly one of `ckpt_to_load_from` and `tensor_name_in_ckpt`
      is specified.
    ValueError: if `initializer` is specified and is not callable.
    RuntimeError: If eager execution is enabled.
  """
  if (dimension is None) or (dimension < 1):
    raise ValueError('Invalid dimension {}.'.format(dimension))
  if (ckpt_to_load_from is None) != (tensor_name_in_ckpt is None):
    raise ValueError('Must specify both `ckpt_to_load_from` and '
                     '`tensor_name_in_ckpt` or none of them.')

  if (initializer is not None) and (not callable(initializer)):
    raise ValueError('initializer must be callable if specified. '
                     'Embedding of column_name: {}'.format(
                         categorical_column.name))
  if initializer is None:
    initializer = init_ops.truncated_normal_initializer(
        mean=0.0, stddev=1 / math.sqrt(dimension))

  return EmbeddingColumn(
      categorical_column=categorical_column,
      dimension=dimension,
      combiner=combiner,
      initializer=initializer,
      ckpt_to_load_from=ckpt_to_load_from,
      tensor_name_in_ckpt=tensor_name_in_ckpt,
      max_norm=max_norm,
      trainable=trainable)


def shared_embedding_columns_v2(categorical_columns,
                                dimension,
                                combiner='mean',
                                initializer=None,
                                shared_embedding_collection_name=None,
                                ckpt_to_load_from=None,
                                tensor_name_in_ckpt=None,
                                max_norm=None,
                                trainable=True):
  """List of dense columns that convert from sparse, categorical input.

  This is similar to `embedding_column`, except that it produces a list of
  embedding columns that share the same embedding weights.

  Use this when your inputs are sparse and of the same type (e.g. watched and
  impression video IDs that share the same vocabulary), and you want to convert
  them to a dense representation (e.g., to feed to a DNN).

  Inputs must be a list of categorical columns created by any of the
  `categorical_column_*` function. They must all be of the same type and have
  the same arguments except `key`. E.g. they can be
  categorical_column_with_vocabulary_file with the same vocabulary_file. Some or
  all columns could also be weighted_categorical_column.

  Here is an example embedding of two features for a DNNClassifier model:

  ```python
  watched_video_id = categorical_column_with_vocabulary_file(
      'watched_video_id', video_vocabulary_file, video_vocabulary_size)
  impression_video_id = categorical_column_with_vocabulary_file(
      'impression_video_id', video_vocabulary_file, video_vocabulary_size)
  columns = shared_embedding_columns(
      [watched_video_id, impression_video_id], dimension=10)

  estimator = tf.estimator.DNNClassifier(feature_columns=columns, ...)

  label_column = ...
  def input_fn():
    features = tf.parse_example(
        ..., features=make_parse_example_spec(columns + [label_column]))
    labels = features.pop(label_column.name)
    return features, labels

  estimator.train(input_fn=input_fn, steps=100)
  ```

  Here is an example using `shared_embedding_columns` with model_fn:

  ```python
  def model_fn(features, ...):
    watched_video_id = categorical_column_with_vocabulary_file(
        'watched_video_id', video_vocabulary_file, video_vocabulary_size)
    impression_video_id = categorical_column_with_vocabulary_file(
        'impression_video_id', video_vocabulary_file, video_vocabulary_size)
    columns = shared_embedding_columns(
        [watched_video_id, impression_video_id], dimension=10)
    dense_tensor = input_layer(features, columns)
    # Form DNN layers, calculate loss, and return EstimatorSpec.
    ...
  ```

  Args:
    categorical_columns: List of categorical columns created by a
      `categorical_column_with_*` function. These columns produce the sparse IDs
      that are inputs to the embedding lookup. All columns must be of the same
      type and have the same arguments except `key`. E.g. they can be
      categorical_column_with_vocabulary_file with the same vocabulary_file.
      Some or all columns could also be weighted_categorical_column.
    dimension: An integer specifying dimension of the embedding, must be > 0.
    combiner: A string specifying how to reduce if there are multiple entries
      in a single row. Currently 'mean', 'sqrtn' and 'sum' are supported, with
      'mean' the default. 'sqrtn' often achieves good accuracy, in particular
      with bag-of-words columns. Each of this can be thought as example level
      normalizations on the column. For more information, see
      `tf.embedding_lookup_sparse`.
    initializer: A variable initializer function to be used in embedding
      variable initialization. If not specified, defaults to
      `tf.truncated_normal_initializer` with mean `0.0` and standard deviation
      `1/sqrt(dimension)`.
    shared_embedding_collection_name: Optional collective name of these columns.
      If not given, a reasonable name will be chosen based on the names of
      `categorical_columns`.
    ckpt_to_load_from: String representing checkpoint name/pattern from which to
      restore column weights. Required if `tensor_name_in_ckpt` is not `None`.
    tensor_name_in_ckpt: Name of the `Tensor` in `ckpt_to_load_from` from
      which to restore the column weights. Required if `ckpt_to_load_from` is
      not `None`.
    max_norm: If not `None`, each embedding is clipped if its l2-norm is
      larger than this value, before combining.
    trainable: Whether or not the embedding is trainable. Default is True.

  Returns:
    A list of dense columns that converts from sparse input. The order of
    results follows the ordering of `categorical_columns`.

  Raises:
    ValueError: if `dimension` not > 0.
    ValueError: if any of the given `categorical_columns` is of different type
      or has different arguments than the others.
    ValueError: if exactly one of `ckpt_to_load_from` and `tensor_name_in_ckpt`
      is specified.
    ValueError: if `initializer` is specified and is not callable.
    RuntimeError: if eager execution is enabled.
  """
  if context.executing_eagerly():
    raise RuntimeError('shared_embedding_columns are not supported when eager '
                       'execution is enabled.')

  if (dimension is None) or (dimension < 1):
    raise ValueError('Invalid dimension {}.'.format(dimension))
  if (ckpt_to_load_from is None) != (tensor_name_in_ckpt is None):
    raise ValueError('Must specify both `ckpt_to_load_from` and '
                     '`tensor_name_in_ckpt` or none of them.')

  if (initializer is not None) and (not callable(initializer)):
    raise ValueError('initializer must be callable if specified.')
  if initializer is None:
    initializer = init_ops.truncated_normal_initializer(
        mean=0.0, stddev=1. / math.sqrt(dimension))

  # Sort the columns so the default collection name is deterministic even if the
  # user passes columns from an unsorted collection, such as dict.values().
  sorted_columns = sorted(categorical_columns, key=lambda x: x.name)

  c0 = sorted_columns[0]
  num_buckets = c0.num_buckets
  if not isinstance(c0, CategoricalColumn):
    raise ValueError(
        'All categorical_columns must be subclasses of CategoricalColumn. '
        'Given: {}, of type: {}'.format(c0, type(c0)))
  if isinstance(c0, WeightedCategoricalColumn):
    c0 = c0.categorical_column
  for c in sorted_columns[1:]:
    if isinstance(c, WeightedCategoricalColumn):
      c = c.categorical_column
    if not isinstance(c, type(c0)):
      raise ValueError(
          'To use shared_embedding_column, all categorical_columns must have '
          'the same type, or be weighted_categorical_column of the same type. '
          'Given column: {} of type: {} does not match given column: {} of '
          'type: {}'.format(c0, type(c0), c, type(c)))
    if num_buckets != c.num_buckets:
      raise ValueError(
          'To use shared_embedding_column, all categorical_columns must have '
          'the same number of buckets. Given column: {} with buckets: {} does  '
          'not match column: {} with buckets: {}'.format(
              c0, num_buckets, c, c.num_buckets))

  if not shared_embedding_collection_name:
    shared_embedding_collection_name = '_'.join(c.name for c in sorted_columns)
    shared_embedding_collection_name += '_shared_embedding'

  result = []
  for column in categorical_columns:
    result.append(
        SharedEmbeddingColumn(
            categorical_column=column,
            initializer=initializer,
            dimension=dimension,
            combiner=combiner,
            shared_embedding_collection_name=shared_embedding_collection_name,
            ckpt_to_load_from=ckpt_to_load_from,
            tensor_name_in_ckpt=tensor_name_in_ckpt,
            max_norm=max_norm,
            trainable=trainable))

  return result


def numeric_column(key,
                   shape=(1,),
                   default_value=None,
                   dtype=dtypes.float32,
                   normalizer_fn=None):
  """Represents real valued or numerical features.

  Example:

  ```python
  price = numeric_column('price')
  columns = [price, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  dense_tensor = input_layer(features, columns)

  # or
  bucketized_price = bucketized_column(price, boundaries=[...])
  columns = [bucketized_price, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction = linear_model(features, columns)
  ```

  Args:
    key: A unique string identifying the input feature. It is used as the
      column name and the dictionary key for feature parsing configs, feature
      `Tensor` objects, and feature columns.
    shape: An iterable of integers specifies the shape of the `Tensor`. An
      integer can be given which means a single dimension `Tensor` with given
      width. The `Tensor` representing the column will have the shape of
      [batch_size] + `shape`.
    default_value: A single value compatible with `dtype` or an iterable of
      values compatible with `dtype` which the column takes on during
      `tf.Example` parsing if data is missing. A default value of `None` will
      cause `tf.parse_example` to fail if an example does not contain this
      column. If a single value is provided, the same value will be applied as
      the default value for every item. If an iterable of values is provided,
      the shape of the `default_value` should be equal to the given `shape`.
    dtype: defines the type of values. Default value is `tf.float32`. Must be a
      non-quantized, real integer or floating point type.
    normalizer_fn: If not `None`, a function that can be used to normalize the
      value of the tensor after `default_value` is applied for parsing.
      Normalizer function takes the input `Tensor` as its argument, and returns
      the output `Tensor`. (e.g. lambda x: (x - 3.0) / 4.2). Please note that
      even though the most common use case of this function is normalization, it
      can be used for any kind of Tensorflow transformations.

  Returns:
    A `NumericColumn`.

  Raises:
    TypeError: if any dimension in shape is not an int
    ValueError: if any dimension in shape is not a positive integer
    TypeError: if `default_value` is an iterable but not compatible with `shape`
    TypeError: if `default_value` is not compatible with `dtype`.
    ValueError: if `dtype` is not convertible to `tf.float32`.
  """
  shape = _check_shape(shape, key)
  if not (dtype.is_integer or dtype.is_floating):
    raise ValueError('dtype must be convertible to float. '
                     'dtype: {}, key: {}'.format(dtype, key))
  default_value = _check_default_value(shape, default_value, dtype, key)

  if normalizer_fn is not None and not callable(normalizer_fn):
    raise TypeError(
        'normalizer_fn must be a callable. Given: {}'.format(normalizer_fn))

  _assert_key_is_string(key)
  return NumericColumn(
      key,
      shape=shape,
      default_value=default_value,
      dtype=dtype,
      normalizer_fn=normalizer_fn)


def bucketized_column(source_column, boundaries):
  """Represents discretized dense input.

  Buckets include the left boundary, and exclude the right boundary. Namely,
  `boundaries=[0., 1., 2.]` generates buckets `(-inf, 0.)`, `[0., 1.)`,
  `[1., 2.)`, and `[2., +inf)`.

  For example, if the inputs are

  ```python
  boundaries = [0, 10, 100]
  input tensor = [[-5, 10000]
                  [150,   10]
                  [5,    100]]
  ```

  then the output will be

  ```python
  output = [[0, 3]
            [3, 2]
            [1, 3]]
  ```

  Example:

  ```python
  price = numeric_column('price')
  bucketized_price = bucketized_column(price, boundaries=[...])
  columns = [bucketized_price, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction = linear_model(features, columns)

  # or
  columns = [bucketized_price, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  dense_tensor = input_layer(features, columns)
  ```

  `bucketized_column` can also be crossed with another categorical column using
  `crossed_column`:

  ```python
  price = numeric_column('price')
  # bucketized_column converts numerical feature to a categorical one.
  bucketized_price = bucketized_column(price, boundaries=[...])
  # 'keywords' is a string feature.
  price_x_keywords = crossed_column([bucketized_price, 'keywords'], 50K)
  columns = [price_x_keywords, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction = linear_model(features, columns)
  ```

  Args:
    source_column: A one-dimensional dense column which is generated with
      `numeric_column`.
    boundaries: A sorted list or tuple of floats specifying the boundaries.

  Returns:
    A `BucketizedColumn`.

  Raises:
    ValueError: If `source_column` is not a numeric column, or if it is not
      one-dimensional.
    ValueError: If `boundaries` is not a sorted list or tuple.
  """
  if not isinstance(source_column, NumericColumn):
    raise ValueError(
        'source_column must be a column generated with numeric_column(). '
        'Given: {}'.format(source_column))
  if len(source_column.shape) > 1:
    raise ValueError(
        'source_column must be one-dimensional column. '
        'Given: {}'.format(source_column))
  if (not boundaries or
      not (isinstance(boundaries, list) or isinstance(boundaries, tuple))):
    raise ValueError('boundaries must be a sorted list.')
  for i in range(len(boundaries) - 1):
    if boundaries[i] >= boundaries[i + 1]:
      raise ValueError('boundaries must be a sorted list.')
  return BucketizedColumn(source_column, tuple(boundaries))


def _assert_string_or_int(dtype, prefix):
  if (dtype != dtypes.string) and (not dtype.is_integer):
    raise ValueError(
        '{} dtype must be string or integer. dtype: {}.'.format(prefix, dtype))


def _assert_key_is_string(key):
  if not isinstance(key, six.string_types):
    raise ValueError(
        'key must be a string. Got: type {}. Given key: {}.'.format(
            type(key), key))


def categorical_column_with_hash_bucket(key,
                                        hash_bucket_size,
                                        dtype=dtypes.string):
  """Represents sparse feature where ids are set by hashing.

  Use this when your sparse features are in string or integer format, and you
  want to distribute your inputs into a finite number of buckets by hashing.
  output_id = Hash(input_feature_string) % bucket_size for string type input.
  For int type input, the value is converted to its string representation first
  and then hashed by the same formula.

  For input dictionary `features`, `features[key]` is either `Tensor` or
  `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int
  and `''` for string, which will be dropped by this feature column.

  Example:

  ```python
  keywords = categorical_column_with_hash_bucket("keywords", 10K)
  columns = [keywords, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction = linear_model(features, columns)

  # or
  keywords_embedded = embedding_column(keywords, 16)
  columns = [keywords_embedded, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  dense_tensor = input_layer(features, columns)
  ```

  Args:
    key: A unique string identifying the input feature. It is used as the
      column name and the dictionary key for feature parsing configs, feature
      `Tensor` objects, and feature columns.
    hash_bucket_size: An int > 1. The number of buckets.
    dtype: The type of features. Only string and integer types are supported.

  Returns:
    A `HashedCategoricalColumn`.

  Raises:
    ValueError: `hash_bucket_size` is not greater than 1.
    ValueError: `dtype` is neither string nor integer.
  """
  if hash_bucket_size is None:
    raise ValueError('hash_bucket_size must be set. ' 'key: {}'.format(key))

  if hash_bucket_size < 1:
    raise ValueError('hash_bucket_size must be at least 1. '
                     'hash_bucket_size: {}, key: {}'.format(
                         hash_bucket_size, key))

  _assert_key_is_string(key)
  _assert_string_or_int(dtype, prefix='column_name: {}'.format(key))

  return HashedCategoricalColumn(key, hash_bucket_size, dtype)


def categorical_column_with_vocabulary_file(key,
                                            vocabulary_file,
                                            vocabulary_size=None,
                                            num_oov_buckets=0,
                                            default_value=None,
                                            dtype=dtypes.string):
  """A `CategoricalColumn` with a vocabulary file.

  Use this when your inputs are in string or integer format, and you have a
  vocabulary file that maps each value to an integer ID. By default,
  out-of-vocabulary values are ignored. Use either (but not both) of
  `num_oov_buckets` and `default_value` to specify how to include
  out-of-vocabulary values.

  For input dictionary `features`, `features[key]` is either `Tensor` or
  `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int
  and `''` for string, which will be dropped by this feature column.

  Example with `num_oov_buckets`:
  File '/us/states.txt' contains 50 lines, each with a 2-character U.S. state
  abbreviation. All inputs with values in that file are assigned an ID 0-49,
  corresponding to its line number. All other values are hashed and assigned an
  ID 50-54.

  ```python
  states = categorical_column_with_vocabulary_file(
      key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
      num_oov_buckets=5)
  columns = [states, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction = linear_model(features, columns)
  ```

  Example with `default_value`:
  File '/us/states.txt' contains 51 lines - the first line is 'XX', and the
  other 50 each have a 2-character U.S. state abbreviation. Both a literal 'XX'
  in input, and other values missing from the file, will be assigned ID 0. All
  others are assigned the corresponding line number 1-50.

  ```python
  states = categorical_column_with_vocabulary_file(
      key='states', vocabulary_file='/us/states.txt', vocabulary_size=51,
      default_value=0)
  columns = [states, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction, _, _ = linear_model(features, columns)
  ```

  And to make an embedding with either:

  ```python
  columns = [embedding_column(states, 3),...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  dense_tensor = input_layer(features, columns)
  ```

  Args:
    key: A unique string identifying the input feature. It is used as the
      column name and the dictionary key for feature parsing configs, feature
      `Tensor` objects, and feature columns.
    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 `CategoricalColumn` with a vocabulary file.

  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.
  """
  if not vocabulary_file:
    raise ValueError('Missing vocabulary_file in {}.'.format(key))

  if vocabulary_size is None:
    if not gfile.Exists(vocabulary_file):
      raise ValueError('vocabulary_file in {} does not exist.'.format(key))

    with gfile.GFile(vocabulary_file) as f:
      vocabulary_size = sum(1 for _ in f)
    logging.info(
        'vocabulary_size = %d in %s is inferred from the number of elements '
        'in the vocabulary_file %s.', vocabulary_size, key, vocabulary_file)

  # `vocabulary_size` isn't required for lookup, but it is for `_num_buckets`.
  if vocabulary_size < 1:
    raise ValueError('Invalid vocabulary_size in {}.'.format(key))
  if num_oov_buckets:
    if default_value is not None:
      raise ValueError(
          'Can\'t specify both num_oov_buckets and default_value in {}.'.format(
              key))
    if num_oov_buckets < 0:
      raise ValueError('Invalid num_oov_buckets {} in {}.'.format(
          num_oov_buckets, key))
  _assert_string_or_int(dtype, prefix='column_name: {}'.format(key))
  _assert_key_is_string(key)
  return VocabularyFileCategoricalColumn(
      key=key,
      vocabulary_file=vocabulary_file,
      vocabulary_size=vocabulary_size,
      num_oov_buckets=0 if num_oov_buckets is None else num_oov_buckets,
      default_value=-1 if default_value is None else default_value,
      dtype=dtype)


def categorical_column_with_vocabulary_list(
    key, vocabulary_list, dtype=None, default_value=-1, num_oov_buckets=0):
  """A `_CategoricalColumn` with in-memory vocabulary.

  Use this when your inputs are in string or integer format, and you have an
  in-memory vocabulary mapping each value to an integer ID. By default,
  out-of-vocabulary values are ignored. Use either (but not both) of
  `num_oov_buckets` and `default_value` to specify how to include
  out-of-vocabulary values.

  For input dictionary `features`, `features[key]` is either `Tensor` or
  `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int
  and `''` for string, which will be dropped by this feature column.

  Example with `num_oov_buckets`:
  In the following example, each input in `vocabulary_list` is assigned an ID
  0-3 corresponding to its index (e.g., input 'B' produces output 2). All other
  inputs are hashed and assigned an ID 4-5.

  ```python
  colors = categorical_column_with_vocabulary_list(
      key='colors', vocabulary_list=('R', 'G', 'B', 'Y'),
      num_oov_buckets=2)
  columns = [colors, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction, _, _ = linear_model(features, columns)
  ```

  Example with `default_value`:
  In the following example, each input in `vocabulary_list` is assigned an ID
  0-4 corresponding to its index (e.g., input 'B' produces output 3). All other
  inputs are assigned `default_value` 0.


  ```python
  colors = categorical_column_with_vocabulary_list(
      key='colors', vocabulary_list=('X', 'R', 'G', 'B', 'Y'), default_value=0)
  columns = [colors, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction, _, _ = linear_model(features, columns)
  ```

  And to make an embedding with either:

  ```python
  columns = [embedding_column(colors, 3),...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  dense_tensor = input_layer(features, columns)
  ```

  Args:
    key: A unique string identifying the input feature. It is used as the
      column name and the dictionary key for feature parsing configs, feature
      `Tensor` objects, and feature columns.
    vocabulary_list: An ordered iterable defining the vocabulary. Each feature
      is mapped to the index of its value (if present) in `vocabulary_list`.
      Must be castable to `dtype`.
    dtype: The type of features. Only string and integer types are supported.
      If `None`, it will be inferred from `vocabulary_list`.
    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`.
    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
      `[len(vocabulary_list), len(vocabulary_list)+num_oov_buckets)` based on a
      hash of the input value. A positive `num_oov_buckets` can not be specified
      with `default_value`.

  Returns:
    A `CategoricalColumn` with in-memory vocabulary.

  Raises:
    ValueError: if `vocabulary_list` is empty, or contains duplicate keys.
    ValueError: `num_oov_buckets` is a negative integer.
    ValueError: `num_oov_buckets` and `default_value` are both specified.
    ValueError: if `dtype` is not integer or string.
  """
  if (vocabulary_list is None) or (len(vocabulary_list) < 1):
    raise ValueError(
        'vocabulary_list {} must be non-empty, column_name: {}'.format(
            vocabulary_list, key))
  if len(set(vocabulary_list)) != len(vocabulary_list):
    raise ValueError(
        'Duplicate keys in vocabulary_list {}, column_name: {}'.format(
            vocabulary_list, key))
  vocabulary_dtype = dtypes.as_dtype(np.array(vocabulary_list).dtype)
  if num_oov_buckets:
    if default_value != -1:
      raise ValueError(
          'Can\'t specify both num_oov_buckets and default_value in {}.'.format(
              key))
    if num_oov_buckets < 0:
      raise ValueError('Invalid num_oov_buckets {} in {}.'.format(
          num_oov_buckets, key))
  _assert_string_or_int(
      vocabulary_dtype, prefix='column_name: {} vocabulary'.format(key))
  if dtype is None:
    dtype = vocabulary_dtype
  elif dtype.is_integer != vocabulary_dtype.is_integer:
    raise ValueError(
        'dtype {} and vocabulary dtype {} do not match, column_name: {}'.format(
            dtype, vocabulary_dtype, key))
  _assert_string_or_int(dtype, prefix='column_name: {}'.format(key))
  _assert_key_is_string(key)

  return VocabularyListCategoricalColumn(
      key=key,
      vocabulary_list=tuple(vocabulary_list),
      dtype=dtype,
      default_value=default_value,
      num_oov_buckets=num_oov_buckets)


def categorical_column_with_identity(key, num_buckets, default_value=None):
  """A `CategoricalColumn` that returns identity values.

  Use this when your inputs are integers in the range `[0, num_buckets)`, and
  you want to use the input value itself as the categorical ID. Values outside
  this range will result in `default_value` if specified, otherwise it will
  fail.

  Typically, this is used for contiguous ranges of integer indexes, but
  it doesn't have to be. This might be inefficient, however, if many of IDs
  are unused. Consider `categorical_column_with_hash_bucket` in that case.

  For input dictionary `features`, `features[key]` is either `Tensor` or
  `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int
  and `''` for string, which will be dropped by this feature column.

  In the following examples, each input in the range `[0, 1000000)` is assigned
  the same value. All other inputs are assigned `default_value` 0. Note that a
  literal 0 in inputs will result in the same default ID.

  Linear model:

  ```python
  video_id = categorical_column_with_identity(
      key='video_id', num_buckets=1000000, default_value=0)
  columns = [video_id, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction, _, _ = linear_model(features, columns)
  ```

  Embedding for a DNN model:

  ```python
  columns = [embedding_column(video_id, 9),...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  dense_tensor = input_layer(features, columns)
  ```

  Args:
    key: A unique string identifying the input feature. It is used as the
      column name and the dictionary key for feature parsing configs, feature
      `Tensor` objects, and feature columns.
    num_buckets: Range of inputs and outputs is `[0, num_buckets)`.
    default_value: If `None`, this column's graph operations will fail for
      out-of-range inputs. Otherwise, this value must be in the range
      `[0, num_buckets)`, and will replace inputs in that range.

  Returns:
    A `CategoricalColumn` that returns identity values.

  Raises:
    ValueError: if `num_buckets` is less than one.
    ValueError: if `default_value` is not in range `[0, num_buckets)`.
  """
  if num_buckets < 1:
    raise ValueError(
        'num_buckets {} < 1, column_name {}'.format(num_buckets, key))
  if (default_value is not None) and (
      (default_value < 0) or (default_value >= num_buckets)):
    raise ValueError(
        'default_value {} not in range [0, {}), column_name {}'.format(
            default_value, num_buckets, key))
  _assert_key_is_string(key)
  return IdentityCategoricalColumn(
      key=key, number_buckets=num_buckets, default_value=default_value)


def indicator_column(categorical_column):
  """Represents multi-hot representation of given categorical column.

  - For DNN model, `indicator_column` can be used to wrap any
    `categorical_column_*` (e.g., to feed to DNN). Consider to Use
    `embedding_column` if the number of buckets/unique(values) are large.

  - For Wide (aka linear) model, `indicator_column` is the internal
    representation for categorical column when passing categorical column
    directly (as any element in feature_columns) to `linear_model`. See
    `linear_model` for details.

  ```python
  name = indicator_column(categorical_column_with_vocabulary_list(
      'name', ['bob', 'george', 'wanda'])
  columns = [name, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  dense_tensor = input_layer(features, columns)

  dense_tensor == [[1, 0, 0]]  # If "name" bytes_list is ["bob"]
  dense_tensor == [[1, 0, 1]]  # If "name" bytes_list is ["bob", "wanda"]
  dense_tensor == [[2, 0, 0]]  # If "name" bytes_list is ["bob", "bob"]
  ```

  Args:
    categorical_column: A `CategoricalColumn` which is created by
      `categorical_column_with_*` or `crossed_column` functions.

  Returns:
    An `IndicatorColumn`.
  """
  return IndicatorColumn(categorical_column)


def weighted_categorical_column(
    categorical_column, weight_feature_key, dtype=dtypes.float32):
  """Applies weight values to a `_CategoricalColumn`.

  Use this when each of your sparse inputs has both an ID and a value. For
  example, if you're representing text documents as a collection of word
  frequencies, you can provide 2 parallel sparse input features ('terms' and
  'frequencies' below).

  Example:

  Input `tf.Example` objects:

  ```proto
  [
    features {
      feature {
        key: "terms"
        value {bytes_list {value: "very" value: "model"}}
      }
      feature {
        key: "frequencies"
        value {float_list {value: 0.3 value: 0.1}}
      }
    },
    features {
      feature {
        key: "terms"
        value {bytes_list {value: "when" value: "course" value: "human"}}
      }
      feature {
        key: "frequencies"
        value {float_list {value: 0.4 value: 0.1 value: 0.2}}
      }
    }
  ]
  ```

  ```python
  categorical_column = categorical_column_with_hash_bucket(
      column_name='terms', hash_bucket_size=1000)
  weighted_column = weighted_categorical_column(
      categorical_column=categorical_column, weight_feature_key='frequencies')
  columns = [weighted_column, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction, _, _ = linear_model(features, columns)
  ```

  This assumes the input dictionary contains a `SparseTensor` for key
  'terms', and a `SparseTensor` for key 'frequencies'. These 2 tensors must have
  the same indices and dense shape.

  Args:
    categorical_column: A `_CategoricalColumn` created by
      `categorical_column_with_*` functions.
    weight_feature_key: String key for weight values.
    dtype: Type of weights, such as `tf.float32`. Only float and integer weights
      are supported.

  Returns:
    A `CategoricalColumn` composed of two sparse features: one represents id,
    the other represents weight (value) of the id feature in that example.

  Raises:
    ValueError: if `dtype` is not convertible to float.
  """
  if (dtype is None) or not (dtype.is_integer or dtype.is_floating):
    raise ValueError('dtype {} is not convertible to float.'.format(dtype))
  return WeightedCategoricalColumn(
      categorical_column=categorical_column,
      weight_feature_key=weight_feature_key,
      dtype=dtype)


def crossed_column(keys, hash_bucket_size, hash_key=None):
  """Returns a column for performing crosses of categorical features.

  Crossed features will be hashed according to `hash_bucket_size`. Conceptually,
  the transformation can be thought of as:
    Hash(cartesian product of features) % `hash_bucket_size`

  For example, if the input features are:

  * SparseTensor referred by first key:

    ```python
    shape = [2, 2]
    {
        [0, 0]: "a"
        [1, 0]: "b"
        [1, 1]: "c"
    }
    ```

  * SparseTensor referred by second key:

    ```python
    shape = [2, 1]
    {
        [0, 0]: "d"
        [1, 0]: "e"
    }
    ```

  then crossed feature will look like:

  ```python
   shape = [2, 2]
  {
      [0, 0]: Hash64("d", Hash64("a")) % hash_bucket_size
      [1, 0]: Hash64("e", Hash64("b")) % hash_bucket_size
      [1, 1]: Hash64("e", Hash64("c")) % hash_bucket_size
  }
  ```

  Here is an example to create a linear model with crosses of string features:

  ```python
  keywords_x_doc_terms = crossed_column(['keywords', 'doc_terms'], 50K)
  columns = [keywords_x_doc_terms, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction = linear_model(features, columns)
  ```

  You could also use vocabulary lookup before crossing:

  ```python
  keywords = categorical_column_with_vocabulary_file(
      'keywords', '/path/to/vocabulary/file', vocabulary_size=1K)
  keywords_x_doc_terms = crossed_column([keywords, 'doc_terms'], 50K)
  columns = [keywords_x_doc_terms, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction = linear_model(features, columns)
  ```

  If an input feature is of numeric type, you can use
  `categorical_column_with_identity`, or `bucketized_column`, as in the example:

  ```python
  # vertical_id is an integer categorical feature.
  vertical_id = categorical_column_with_identity('vertical_id', 10K)
  price = numeric_column('price')
  # bucketized_column converts numerical feature to a categorical one.
  bucketized_price = bucketized_column(price, boundaries=[...])
  vertical_id_x_price = crossed_column([vertical_id, bucketized_price], 50K)
  columns = [vertical_id_x_price, ...]
  features = tf.parse_example(..., features=make_parse_example_spec(columns))
  linear_prediction = linear_model(features, columns)
  ```

  To use crossed column in DNN model, you need to add it in an embedding column
  as in this example:

  ```python
  vertical_id_x_price = crossed_column([vertical_id, bucketized_price], 50K)
  vertical_id_x_price_embedded = embedding_column(vertical_id_x_price, 10)
  dense_tensor = input_layer(features, [vertical_id_x_price_embedded, ...])
  ```

  Args:
    keys: An iterable identifying the features to be crossed. Each element can
      be either:
      * string: Will use the corresponding feature which must be of string type.
      * `CategoricalColumn`: Will use the transformed tensor produced by this
        column. Does not support hashed categorical column.
    hash_bucket_size: An int > 1. The number of buckets.
    hash_key: Specify the hash_key that will be used by the `FingerprintCat64`
      function to combine the crosses fingerprints on SparseCrossOp (optional).

  Returns:
    A `CrossedColumn`.

  Raises:
    ValueError: If `len(keys) < 2`.
    ValueError: If any of the keys is neither a string nor `CategoricalColumn`.
    ValueError: If any of the keys is `HashedCategoricalColumn`.
    ValueError: If `hash_bucket_size < 1`.
  """
  if not hash_bucket_size or hash_bucket_size < 1:
    raise ValueError('hash_bucket_size must be > 1. '
                     'hash_bucket_size: {}'.format(hash_bucket_size))
  if not keys or len(keys) < 2:
    raise ValueError(
        'keys must be a list with length > 1. Given: {}'.format(keys))
  for key in keys:
    if (not isinstance(key, six.string_types) and
        not isinstance(key, CategoricalColumn)):
      raise ValueError(
          'Unsupported key type. All keys must be either string, or '
          'categorical column except HashedCategoricalColumn. '
          'Given: {}'.format(key))
    if isinstance(key, HashedCategoricalColumn):
      raise ValueError(
          'categorical_column_with_hash_bucket is not supported for crossing. '
          'Hashing before crossing will increase probability of collision. '
          'Instead, use the feature name as a string. Given: {}'.format(key))
  return CrossedColumn(
      keys=tuple(keys), hash_bucket_size=hash_bucket_size, hash_key=hash_key)


class FeatureColumn(object):
  """Represents a feature column abstraction.

  WARNING: Do not subclass this layer unless you know what you are doing:
  the API is subject to future changes.

  To distinguish between the concept of a feature family and a specific binary
  feature within a family, we refer to a feature family like "country" as a
  feature column. For example, we can have a feature in a `tf.Example` format:
    {key: "country",  value: [ "US" ]}
  In this example the value of feature is "US" and "country" refers to the
  column of the feature.

  This class is an abstract class. Users should not create instances of this.
  """
  __metaclass__ = abc.ABCMeta

  @abc.abstractproperty
  def name(self):
    """Returns string. Used for naming."""
    pass

  @abc.abstractmethod
  def transform_feature(self, transformation_cache, state_manager):
    """Returns intermediate representation (usually a `Tensor`).

    Uses `transformation_cache` to create an intermediate representation
    (usually a `Tensor`) that other feature columns can use.

    Example usage of `transformation_cache`:
    Let's say a Feature column depends on raw feature ('raw') and another
    `FeatureColumn` (input_fc). To access corresponding `Tensor`s,
    transformation_cache will be used as follows:

    ```python
    raw_tensor = transformation_cache.get('raw', state_manager)
    fc_tensor = transformation_cache.get(input_fc, state_manager)
    ```

    Args:
      transformation_cache: A `FeatureTransformationCache` object to access
        features.
      state_manager: A `StateManager` to create / access resources such as
        lookup tables.

    Returns:
      Transformed feature `Tensor`.
    """
    pass

  @abc.abstractproperty
  def parse_example_spec(self):
    """Returns a `tf.Example` parsing spec as dict.

    It is used for get_parsing_spec for `tf.parse_example`. Returned spec is a
    dict from keys ('string') to `VarLenFeature`, `FixedLenFeature`, and other
    supported objects. Please check documentation of `tf.parse_example` for all
    supported spec objects.

    Let's say a Feature column depends on raw feature ('raw') and another
    `FeatureColumn` (input_fc). One possible implementation of
    parse_example_spec is as follows:

    ```python
    spec = {'raw': tf.FixedLenFeature(...)}
    spec.update(input_fc.parse_example_spec)
    return spec
    ```
    """
    pass

  def create_state(self, state_manager):
    """Uses the `state_manager` to create state for the FeatureColumn.

    Args:
      state_manager: A `StateManager` to create / access resources such as
        lookup tables and variables.
    """
    pass


class DenseColumn(FeatureColumn):
  """Represents a column which can be represented as `Tensor`.

  Some examples of this type are: numeric_column, embedding_column,
  indicator_column.
  """

  __metaclass__ = abc.ABCMeta

  @abc.abstractproperty
  def variable_shape(self):
    """`TensorShape` of `get_dense_tensor`, without batch dimension."""
    pass

  @abc.abstractmethod
  def get_dense_tensor(self, transformation_cache, state_manager):
    """Returns a `Tensor`.

    The output of this function will be used by model-builder-functions. For
    example the pseudo code of `input_layer` will be like:

    ```python
    def input_layer(features, feature_columns, ...):
      outputs = [fc.get_dense_tensor(...) for fc in feature_columns]
      return tf.concat(outputs)
    ```

    Args:
      transformation_cache: A `FeatureTransformationCache` object to access
        features.
      state_manager: A `StateManager` to create / access resources such as
        lookup tables.

    Returns:
      `Tensor` of shape [batch_size] + `variable_shape`.
    """
    pass


def is_feature_column_v2(feature_columns):
  """Returns True if all feature columns are V2."""
  for feature_column in feature_columns:
    if not isinstance(feature_column, FeatureColumn):
      return False
  return True


def _create_weighted_sum(column, transformation_cache, state_manager,
                         sparse_combiner, weight_var):
  """Creates a weighted sum for a dense/categorical column for linear_model."""
  if isinstance(column, CategoricalColumn):
    return _create_categorical_column_weighted_sum(
        column=column,
        transformation_cache=transformation_cache,
        state_manager=state_manager,
        sparse_combiner=sparse_combiner,
        weight_var=weight_var)
  else:
    return _create_dense_column_weighted_sum(
        column=column,
        transformation_cache=transformation_cache,
        state_manager=state_manager,
        weight_var=weight_var)


def _create_dense_column_weighted_sum(column, transformation_cache,
                                      state_manager, weight_var):
  """Create a weighted sum of a dense column for linear_model."""
  tensor = column.get_dense_tensor(transformation_cache, state_manager)
  num_elements = column.variable_shape.num_elements()
  batch_size = array_ops.shape(tensor)[0]
  tensor = array_ops.reshape(tensor, shape=(batch_size, num_elements))
  return math_ops.matmul(tensor, weight_var, name='weighted_sum')


class CategoricalColumn(FeatureColumn):
  """Represents a categorical feature.

  A categorical feature typically handled with a `tf.SparseTensor` of IDs.
  """
  __metaclass__ = abc.ABCMeta

  IdWeightPair = collections.namedtuple(  # pylint: disable=invalid-name
      'IdWeightPair', ('id_tensor', 'weight_tensor'))

  @abc.abstractproperty
  def num_buckets(self):
    """Returns number of buckets in this sparse feature."""
    pass

  @abc.abstractmethod
  def get_sparse_tensors(self, transformation_cache, state_manager):
    """Returns an IdWeightPair.

    `IdWeightPair` is a pair of `SparseTensor`s which represents ids and
    weights.

    `IdWeightPair.id_tensor` is typically a `batch_size` x `num_buckets`
    `SparseTensor` of `int64`. `IdWeightPair.weight_tensor` is either a
    `SparseTensor` of `float` or `None` to indicate all weights should be
    taken to be 1. If specified, `weight_tensor` must have exactly the same
    shape and indices as `sp_ids`. Expected `SparseTensor` is same as parsing
    output of a `VarLenFeature` which is a ragged matrix.

    Args:
      transformation_cache: A `FeatureTransformationCache` object to access
        features.
      state_manager: A `StateManager` to create / access resources such as
        lookup tables.
    """
    pass


def _create_categorical_column_weighted_sum(
    column, transformation_cache, state_manager, sparse_combiner, weight_var):
  # pylint: disable=g-doc-return-or-yield,g-doc-args
  """Create a weighted sum of a categorical column for linear_model.

  Note to maintainer: As implementation details, the weighted sum is
  implemented via embedding_lookup_sparse toward efficiency. Mathematically,
  they are the same.

  To be specific, conceptually, categorical column can be treated as multi-hot
  vector. Say:

  ```python
    x = [0 0 1]  # categorical column input
    w = [a b c]  # weights
  ```
  The weighted sum is `c` in this case, which is same as `w[2]`.

  Another example is

  ```python
    x = [0 1 1]  # categorical column input
    w = [a b c]  # weights
  ```
  The weighted sum is `b + c` in this case, which is same as `w[2] + w[3]`.

  For both cases, we can implement weighted sum via embedding_lookup with
  sparse_combiner = "sum".
  """

  sparse_tensors = column.get_sparse_tensors(transformation_cache,
                                             state_manager)
  id_tensor = sparse_ops.sparse_reshape(sparse_tensors.id_tensor, [
      array_ops.shape(sparse_tensors.id_tensor)[0], -1
  ])
  weight_tensor = sparse_tensors.weight_tensor
  if weight_tensor is not None:
    weight_tensor = sparse_ops.sparse_reshape(
        weight_tensor, [array_ops.shape(weight_tensor)[0], -1])

  return _safe_embedding_lookup_sparse(
      weight_var,
      id_tensor,
      sparse_weights=weight_tensor,
      combiner=sparse_combiner,
      name='weighted_sum')


class SequenceDenseColumn(FeatureColumn):
  """Represents dense sequence data."""

  __metaclass__ = abc.ABCMeta

  TensorSequenceLengthPair = collections.namedtuple(  # pylint: disable=invalid-name
      'TensorSequenceLengthPair', ('dense_tensor', 'sequence_length'))

  @abc.abstractmethod
  def get_sequence_dense_tensor(self, transformation_cache, state_manager):
    """Returns a `TensorSequenceLengthPair`.

    Args:
      transformation_cache: A `FeatureTransformationCache` object to access
        features.
      state_manager: A `StateManager` to create / access resources such as
        lookup tables.
    """
    pass


class FeatureTransformationCache(object):
  """Handles caching of transformations while building the model.

  `FeatureColumn` specifies how to digest an input column to the network. Some
  feature columns require data transformations. This class caches those
  transformations.

  Some features may be used in more than one place. For example, one can use a
  bucketized feature by itself and a cross with it. In that case we
  should create only one bucketization op instead of creating ops for each
  feature column separately. To handle re-use of transformed columns,
  `FeatureTransformationCache` caches all previously transformed columns.

  Example:
  We're trying to use the following `FeatureColumn`s:

  ```python
  bucketized_age = fc.bucketized_column(fc.numeric_column("age"), ...)
  keywords = fc.categorical_column_with_hash_buckets("keywords", ...)
  age_X_keywords = fc.crossed_column([bucketized_age, "keywords"])
  ... = linear_model(features,
                          [bucketized_age, keywords, age_X_keywords]
  ```

  If we transform each column independently, then we'll get duplication of
  bucketization (one for cross, one for bucketization itself).
  The `FeatureTransformationCache` eliminates this duplication.
  """

  def __init__(self, features):
    """Creates a `FeatureTransformationCache`.

    Args:
      features: A mapping from feature column to objects that are `Tensor` or
        `SparseTensor`, or can be converted to same via
        `sparse_tensor.convert_to_tensor_or_sparse_tensor`. A `string` key
        signifies a base feature (not-transformed). A `FeatureColumn` key
        means that this `Tensor` is the output of an existing `FeatureColumn`
        which can be reused.
    """
    self._features = features.copy()
    self._feature_tensors = {}

  def get(self, key, state_manager):
    """Returns a `Tensor` for the given key.

    A `str` key is used to access a base feature (not-transformed). When a
    `FeatureColumn` is passed, the transformed feature is returned if it
    already exists, otherwise the given `FeatureColumn` is asked to provide its
    transformed output, which is then cached.

    Args:
      key: a `str` or a `FeatureColumn`.
      state_manager: A StateManager object that holds the FeatureColumn state.

    Returns:
      The transformed `Tensor` corresponding to the `key`.

    Raises:
      ValueError: if key is not found or a transformed `Tensor` cannot be
        computed.
    """
    if key in self._feature_tensors:
      # FeatureColumn is already transformed or converted.
      return self._feature_tensors[key]

    if key in self._features:
      feature_tensor = self._get_raw_feature_as_tensor(key)
      self._feature_tensors[key] = feature_tensor
      return feature_tensor

    if isinstance(key, six.string_types):
      raise ValueError('Feature {} is not in features dictionary.'.format(key))

    if not isinstance(key, FeatureColumn):
      raise TypeError('"key" must be either a "str" or "FeatureColumn". '
                      'Provided: {}'.format(key))

    column = key
    logging.debug('Transforming feature_column %s.', column)
    transformed = column.transform_feature(self, state_manager)
    if transformed is None:
      raise ValueError('Column {} is not supported.'.format(column.name))
    self._feature_tensors[column] = transformed
    return transformed

  def _get_raw_feature_as_tensor(self, key):
    """Gets the raw_feature (keyed by `key`) as `tensor`.

    The raw feature is converted to (sparse) tensor and maybe expand dim.

    For both `Tensor` and `SparseTensor`, the rank will be expanded (to 2) if
    the rank is 1. This supports dynamic rank also. For rank 0 raw feature, will
    error out as it is not supported.

    Args:
      key: A `str` key to access the raw feature.

    Returns:
      A `Tensor` or `SparseTensor`.

    Raises:
      ValueError: if the raw feature has rank 0.
    """
    raw_feature = self._features[key]
    feature_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
        raw_feature)

    def expand_dims(input_tensor):
      # Input_tensor must have rank 1.
      if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
        return sparse_ops.sparse_reshape(
            input_tensor, [array_ops.shape(input_tensor)[0], 1])
      else:
        return array_ops.expand_dims(input_tensor, -1)

    rank = feature_tensor.get_shape().ndims
    if rank is not None:
      if rank == 0:
        raise ValueError(
            'Feature (key: {}) cannot have rank 0. Give: {}'.format(
                key, feature_tensor))
      return feature_tensor if rank != 1 else expand_dims(feature_tensor)

    # Handle dynamic rank.
    with ops.control_dependencies([
        check_ops.assert_positive(
            array_ops.rank(feature_tensor),
            message='Feature (key: {}) cannot have rank 0. Given: {}'.format(
                key, feature_tensor))]):
      return control_flow_ops.cond(
          math_ops.equal(1, array_ops.rank(feature_tensor)),
          lambda: expand_dims(feature_tensor),
          lambda: feature_tensor)


# TODO(ptucker): Move to third_party/tensorflow/python/ops/sparse_ops.py
def _shape_offsets(shape):
  """Returns moving offset for each dimension given shape."""
  offsets = []
  for dim in reversed(shape):
    if offsets:
      offsets.append(dim * offsets[-1])
    else:
      offsets.append(dim)
  offsets.reverse()
  return offsets


# TODO(ptucker): Move to third_party/tensorflow/python/ops/sparse_ops.py
def _to_sparse_input_and_drop_ignore_values(input_tensor, ignore_value=None):
  """Converts a `Tensor` to a `SparseTensor`, dropping ignore_value cells.

  If `input_tensor` is already a `SparseTensor`, just return it.

  Args:
    input_tensor: A string or integer `Tensor`.
    ignore_value: Entries in `dense_tensor` equal to this value will be
      absent from the resulting `SparseTensor`. If `None`, default value of
      `dense_tensor`'s dtype will be used ('' for `str`, -1 for `int`).

  Returns:
    A `SparseTensor` with the same shape as `input_tensor`.

  Raises:
    ValueError: when `input_tensor`'s rank is `None`.
  """
  input_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
      input_tensor)
  if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
    return input_tensor
  with ops.name_scope(None, 'to_sparse_input', (input_tensor, ignore_value,)):
    if ignore_value is None:
      if input_tensor.dtype == dtypes.string:
        # Exception due to TF strings are converted to numpy objects by default.
        ignore_value = ''
      elif input_tensor.dtype.is_integer:
        ignore_value = -1  # -1 has a special meaning of missing feature
      else:
        # NOTE: `as_numpy_dtype` is a property, so with the parentheses this is
        # constructing a new numpy object of the given type, which yields the
        # default value for that type.
        ignore_value = input_tensor.dtype.as_numpy_dtype()
    ignore_value = math_ops.cast(
        ignore_value, input_tensor.dtype, name='ignore_value')
    indices = array_ops.where(
        math_ops.not_equal(input_tensor, ignore_value), name='indices')
    return sparse_tensor_lib.SparseTensor(
        indices=indices,
        values=array_ops.gather_nd(input_tensor, indices, name='values'),
        dense_shape=array_ops.shape(
            input_tensor, out_type=dtypes.int64, name='dense_shape'))


def _normalize_feature_columns(feature_columns):
  """Normalizes the `feature_columns` input.

  This method converts the `feature_columns` to list type as best as it can. In
  addition, verifies the type and other parts of feature_columns, required by
  downstream library.

  Args:
    feature_columns: The raw feature columns, usually passed by users.

  Returns:
    The normalized feature column list.

  Raises:
    ValueError: for any invalid inputs, such as empty, duplicated names, etc.
  """
  if isinstance(feature_columns, FeatureColumn):
    feature_columns = [feature_columns]

  if isinstance(feature_columns, collections.Iterator):
    feature_columns = list(feature_columns)

  if isinstance(feature_columns, dict):
    raise ValueError('Expected feature_columns to be iterable, found dict.')

  for column in feature_columns:
    if not isinstance(column, FeatureColumn):
      raise ValueError('Items of feature_columns must be a FeatureColumn. '
                       'Given (type {}): {}.'.format(type(column), column))
  if not feature_columns:
    raise ValueError('feature_columns must not be empty.')
  name_to_column = dict()
  for column in feature_columns:
    if column.name in name_to_column:
      raise ValueError('Duplicate feature column name found for columns: {} '
                       'and {}. This usually means that these columns refer to '
                       'same base feature. Either one must be discarded or a '
                       'duplicated but renamed item must be inserted in '
                       'features dict.'.format(column,
                                               name_to_column[column.name]))
    name_to_column[column.name] = column

  return feature_columns


class NumericColumn(
    DenseColumn,
    collections.namedtuple(
        'NumericColumn',
        ('key', 'shape', 'default_value', 'dtype', 'normalizer_fn'))):
  """see `numeric_column`."""

  @property
  def name(self):
    """See `FeatureColumn` base class."""
    return self.key

  @property
  def parse_example_spec(self):
    """See `FeatureColumn` base class."""
    return {
        self.key:
            parsing_ops.FixedLenFeature(self.shape, self.dtype,
                                        self.default_value)
    }

  def transform_feature(self, transformation_cache, state_manager):
    """See `FeatureColumn` base class.

    In this case, we apply the `normalizer_fn` to the input tensor.

    Args:
      transformation_cache: A `FeatureTransformationCache` object to access
        features.
      state_manager: A `StateManager` to create / access resources such as
        lookup tables.

    Returns:
      Normalized input tensor.
    Raises:
      ValueError: If a SparseTensor is passed in.
    """
    input_tensor = transformation_cache.get(self.key, state_manager)
    if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
      raise ValueError(
          'The corresponding Tensor of numerical column must be a Tensor. '
          'SparseTensor is not supported. key: {}'.format(self.key))
    if self.normalizer_fn is not None:
      input_tensor = self.normalizer_fn(input_tensor)
    return math_ops.to_float(input_tensor)

  @property
  def variable_shape(self):
    """See `DenseColumn` base class."""
    return tensor_shape.TensorShape(self.shape)

  def get_dense_tensor(self, transformation_cache, state_manager):
    """Returns dense `Tensor` representing numeric feature.

    Args:
      transformation_cache: A `FeatureTransformationCache` object to access
        features.
      state_manager: A `StateManager` to create / access resources such as
        lookup tables.

    Returns:
      Dense `Tensor` created within `transform_feature`.
    """
    # Feature has been already transformed. Return the intermediate
    # representation created by _transform_feature.
    return transformation_cache.get(self, state_manager)


class BucketizedColumn(DenseColumn, CategoricalColumn,
                       collections.namedtuple('BucketizedColumn',
                                              ('source_column', 'boundaries'))):
  """See `bucketized_column`."""

  @property
  def name(self):
    """See `FeatureColumn` base class."""
    return '{}_bucketized'.format(self.source_column.name)

  @property
  def parse_example_spec(self):
    """See `FeatureColumn` base class."""
    return self.source_column.parse_example_spec

  def transform_feature(self, transformation_cache, state_manager):
    """Returns bucketized categorical `source_column` tensor."""
    source_tensor = transformation_cache.get(self.source_column, state_manager)
    return math_ops._bucketize(  # pylint: disable=protected-access
        source_tensor,
        boundaries=self.boundaries)

  @property
  def variable_shape(self):
    """See `DenseColumn` base class."""
    return tensor_shape.TensorShape(
        tuple(self.source_column.shape) + (len(self.boundaries) + 1,))

  def get_dense_tensor(self, transformation_cache, state_manager):
    """Returns one hot encoded dense `Tensor`."""
    input_tensor = transformation_cache.get(self, state_manager)
    return array_ops.one_hot(
        indices=math_ops.to_int64(input_tensor),
        depth=len(self.boundaries) + 1,
        on_value=1.,
        off_value=0.)

  @property
  def num_buckets(self):
    """See `CategoricalColumn` base class."""
    # By construction, source_column is always one-dimensional.
    return (len(self.boundaries) + 1) * self.source_column.shape[0]

  def get_sparse_tensors(self, transformation_cache, state_manager):
    """Converts dense inputs to SparseTensor so downstream code can use it."""
    input_tensor = transformation_cache.get(self, state_manager)
    batch_size = array_ops.shape(input_tensor)[0]
    # By construction, source_column is always one-dimensional.
    source_dimension = self.source_column.shape[0]

    i1 = array_ops.reshape(
        array_ops.tile(
            array_ops.expand_dims(math_ops.range(0, batch_size), 1),
            [1, source_dimension]),
        (-1,))
    i2 = array_ops.tile(math_ops.range(0, source_dimension), [batch_size])
    # Flatten the bucket indices and unique them across dimensions
    # E.g. 2nd dimension indices will range from k to 2*k-1 with k buckets
    bucket_indices = (
        array_ops.reshape(input_tensor, (-1,)) +
        (len(self.boundaries) + 1) * i2)

    indices = math_ops.to_int64(array_ops.transpose(array_ops.stack((i1, i2))))
    dense_shape = math_ops.to_int64(array_ops.stack(
        [batch_size, source_dimension]))
    sparse_tensor = sparse_tensor_lib.SparseTensor(
        indices=indices,
        values=bucket_indices,
        dense_shape=dense_shape)
    return CategoricalColumn.IdWeightPair(sparse_tensor, None)


class EmbeddingColumn(
    DenseColumn, SequenceDenseColumn,
    collections.namedtuple(
        'EmbeddingColumn',
        ('categorical_column', 'dimension', 'combiner', 'initializer',
         'ckpt_to_load_from', 'tensor_name_in_ckpt', 'max_norm', 'trainable'))):
  """See `embedding_column`."""

  @property
  def name(self):
    """See `FeatureColumn` base class."""
    return '{}_embedding'.format(self.categorical_column.name)

  @property
  def parse_example_spec(self):
    """See `FeatureColumn` base class."""
    return self.categorical_column.parse_example_spec

  def transform_feature(self, transformation_cache, state_manager):
    """Transforms underlying `categorical_column`."""
    return transformation_cache.get(self.categorical_column, state_manager)

  @property
  def variable_shape(self):
    """See `DenseColumn` base class."""
    return tensor_shape.vector(self.dimension)

  def create_state(self, state_manager):
    """Creates the embedding lookup variable."""
    embedding_shape = (self.categorical_column.num_buckets, self.dimension)
    state_manager.create_variable(
        self,
        name='embedding_weights',
        shape=embedding_shape,
        dtype=dtypes.float32,
        trainable=self.trainable,
        initializer=self.initializer)

  def _get_dense_tensor_internal(self, transformation_cache, state_manager):
    """Private method that follows the signature of _get_dense_tensor."""
    # Get sparse IDs and weights.
    sparse_tensors = self.categorical_column.get_sparse_tensors(
        transformation_cache, state_manager)
    sparse_ids = sparse_tensors.id_tensor
    sparse_weights = sparse_tensors.weight_tensor

    embedding_weights = state_manager.get_variable(
        self, name='embedding_weights')

    if self.ckpt_to_load_from is not None:
      to_restore = embedding_weights
      if isinstance(to_restore, variables.PartitionedVariable):
        to_restore = to_restore._get_variable_list()  # pylint: disable=protected-access
      checkpoint_utils.init_from_checkpoint(self.ckpt_to_load_from, {
          self.tensor_name_in_ckpt: to_restore
      })

    # Return embedding lookup result.
    return _safe_embedding_lookup_sparse(
        embedding_weights=embedding_weights,
        sparse_ids=sparse_ids,
        sparse_weights=sparse_weights,
        combiner=self.combiner,
        name='%s_weights' % self.name,
        max_norm=self.max_norm)

  def get_dense_tensor(self, transformation_cache, state_manager):
    """Returns tensor after doing the embedding lookup.

    Args:
      transformation_cache: A `FeatureTransformationCache` object to access
        features.
      state_manager: A `StateManager` to create / access resources such as
        lookup tables.

    Returns:
      Embedding lookup tensor.

    Raises:
      ValueError: `categorical_column` is SequenceCategoricalColumn.
    """
    if isinstance(self.categorical_column, SequenceCategoricalColumn):
      raise ValueError(
          'In embedding_column: {}. '
          'categorical_column must not be of type SequenceCategoricalColumn. '
          'Suggested fix A: If you wish to use input_layer, use a '
          'non-sequence categorical_column_with_*. '
          'Suggested fix B: If you wish to create sequence input, use '
          'sequence_input_layer instead of input_layer. '
          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
                                       self.categorical_column))
    return self._get_dense_tensor_internal(transformation_cache, state_manager)

  def get_sequence_dense_tensor(self, transformation_cache, state_manager):
    """See `SequenceDenseColumn` base class."""
    if not isinstance(self.categorical_column, SequenceCategoricalColumn):
      raise ValueError(
          'In embedding_column: {}. '
          'categorical_column must be of type SequenceCategoricalColumn '
          'to use sequence_input_layer. '
          'Suggested fix: Use one of sequence_categorical_column_with_*. '
          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
                                       self.categorical_column))
    dense_tensor = self._get_dense_tensor_internal(  # pylint: disable=protected-access
        transformation_cache, state_manager)
    sparse_tensors = self.categorical_column.get_sparse_tensors(
        transformation_cache, state_manager)
    sequence_length = _sequence_length_from_sparse_tensor(
        sparse_tensors.id_tensor)
    return SequenceDenseColumn.TensorSequenceLengthPair(
        dense_tensor=dense_tensor, sequence_length=sequence_length)


def _get_graph_for_variable(var):
  if isinstance(var, variables.PartitionedVariable):
    return list(var)[0].graph
  else:
    return var.graph


class SharedEmbeddingStateManager(Layer):
  """A state manager that handle the state of shared embedding columns.

  This can handle multiple sets of columns that share variables."""

  def __init__(self, trainable=True, name=None, **kwargs):
    """Constructs a `SharedEmbeddingStateManager`.

    Args:
      trainable: If true, variables created are trainable.
      name: Name of the State Manager.
      **kwargs: Keyword arguments.
    """
    super(SharedEmbeddingStateManager, self).__init__(
        name=name, trainable=trainable, **kwargs)
    self._var_dict = {}

  def create_variable(self,
                      name,
                      shape,
                      dtype=None,
                      trainable=True,
                      initializer=None):
    """Creates a variable.

    Makes sure only one var is created per `shared_collection_name`. `name` is
    ignored here as the variable is named `shared_collection_name` instead.

    Args:
      name: Name of the variable. Not used.
      shape: Variable shape.
      dtype: Variable type.
      trainable: If variable created should be trainable or not.
      initializer: Variable initializer.

    Returns:
      A variable or partitioned variable.
    """
    if name in self._var_dict:
      var = self._var_dict[name]
      return var
    with variable_scope.variable_scope(
        self.name, reuse=variable_scope.AUTO_REUSE):
      var = self.add_variable(
          name=name,
          shape=shape,
          dtype=dtype,
          trainable=self.trainable and trainable,
          initializer=initializer,
          use_resource=True,
          # TODO(rohanj): Get rid of this hack once we have a mechanism for
          # specifying a default partitioner for an entire layer. In that case,
          # the default getter for Layers should work.
          getter=variable_scope.get_variable)
    self._var_dict[name] = var
    return var

  def get_variable(self, feature_column, name):
    if name not in self._var_dict:
      raise ValueError('Variable name: {} not recognized.'.format(name))
    return self._var_dict[name]


def maybe_create_shared_state_manager(feature_columns):
  if is_feature_column_v2(feature_columns):
    return SharedEmbeddingStateManager()
  return None


class SharedEmbeddingColumn(
    DenseColumn, SequenceDenseColumn,
    collections.namedtuple(
        'SharedEmbeddingColumn',
        ('categorical_column', 'dimension', 'combiner', 'initializer',
         'shared_embedding_collection_name', 'ckpt_to_load_from',
         'tensor_name_in_ckpt', 'max_norm', 'trainable'))):
  """See `embedding_column`."""

  @property
  def name(self):
    """See `FeatureColumn` base class."""
    return '{}_shared_embedding'.format(self.categorical_column.name)

  @property
  def shared_collection_name(self):
    """Returns the shared name of this column.

    A group of columns share an embedding. Each one of those columns would have
    the same `shared_collection_name` by which they could be collectively
    referred to.
    """
    return self.shared_embedding_collection_name

  @property
  def parse_example_spec(self):
    """See `FeatureColumn` base class."""
    return self.categorical_column.parse_example_spec

  def transform_feature(self, transformation_cache, state_manager):
    """See `FeatureColumn` base class."""
    return transformation_cache.get(self.categorical_column, state_manager)

  @property
  def variable_shape(self):
    """See `DenseColumn` base class."""
    return tensor_shape.vector(self.dimension)

  def create_state(self, state_manager):
    """Creates the shared embedding lookup variable."""
    if not isinstance(state_manager, SharedEmbeddingStateManager):
      raise ValueError('Expected state_manager to be of type '
                       'SharedEmbeddingStateManager. Obtained type: {}'.format(
                           type(state_manager)))
    embedding_shape = (self.categorical_column.num_buckets, self.dimension)
    state_manager.create_variable(
        name=self.shared_collection_name,
        shape=embedding_shape,
        dtype=dtypes.float32,
        trainable=self.trainable,
        initializer=self.initializer)

  def _get_dense_tensor_internal(self, transformation_cache, state_manager):
    """Private method that follows the signature of _get_dense_tensor."""
    # This method is called from a variable_scope with name _var_scope_name,
    # which is shared among all shared embeddings. Open a name_scope here, so
    # that the ops for different columns have distinct names.
    with ops.name_scope(None, default_name=self.name):
      # Get sparse IDs and weights.
      sparse_tensors = self.categorical_column.get_sparse_tensors(
          transformation_cache, state_manager)
      sparse_ids = sparse_tensors.id_tensor
      sparse_weights = sparse_tensors.weight_tensor

      embedding_weights = state_manager.get_variable(
          self, name=self.shared_collection_name)

      if self.ckpt_to_load_from is not None:
        to_restore = embedding_weights
        if isinstance(to_restore, variables.PartitionedVariable):
          to_restore = to_restore._get_variable_list()  # pylint: disable=protected-access
        checkpoint_utils.init_from_checkpoint(self.ckpt_to_load_from, {
            self.tensor_name_in_ckpt: to_restore
        })

      # Return embedding lookup result.
      return _safe_embedding_lookup_sparse(
          embedding_weights=embedding_weights,
          sparse_ids=sparse_ids,
          sparse_weights=sparse_weights,
          combiner=self.combiner,
          name='%s_weights' % self.name,
          max_norm=self.max_norm)

  def get_dense_tensor(self, transformation_cache, state_manager):
    """Returns the embedding lookup result."""
    if isinstance(self.categorical_column, SequenceCategoricalColumn):
      raise ValueError(
          'In embedding_column: {}. '
          'categorical_column must not be of type SequenceCategoricalColumn. '
          'Suggested fix A: If you wish to use input_layer, use a '
          'non-sequence categorical_column_with_*. '
          'Suggested fix B: If you wish to create sequence input, use '
          'sequence_input_layer instead of input_layer. '
          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
                                       self.categorical_column))
    return self._get_dense_tensor_internal(transformation_cache, state_manager)

  def get_sequence_dense_tensor(self, transformation_cache, state_manager):
    """See `SequenceDenseColumn` base class."""
    if not isinstance(self.categorical_column, SequenceCategoricalColumn):
      raise ValueError(
          'In embedding_column: {}. '
          'categorical_column must be of type SequenceCategoricalColumn '
          'to use sequence_input_layer. '
          'Suggested fix: Use one of sequence_categorical_column_with_*. '
          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
                                       self.categorical_column))
    dense_tensor = self.get_dense_tensor_internal(transformation_cache,
                                                  state_manager)
    sparse_tensors = self.categorical_column.get_sparse_tensors(
        transformation_cache, state_manager)
    sequence_length = _sequence_length_from_sparse_tensor(
        sparse_tensors.id_tensor)
    return SequenceDenseColumn.TensorSequenceLengthPair(
        dense_tensor=dense_tensor, sequence_length=sequence_length)


def _create_tuple(shape, value):
  """Returns a tuple with given shape and filled with value."""
  if shape:
    return tuple([_create_tuple(shape[1:], value) for _ in range(shape[0])])
  return value


def _as_tuple(value):
  if not nest.is_sequence(value):
    return value
  return tuple([_as_tuple(v) for v in value])


def _check_shape(shape, key):
  """Returns shape if it's valid, raises error otherwise."""
  assert shape is not None
  if not nest.is_sequence(shape):
    shape = [shape]
  shape = tuple(shape)
  for dimension in shape:
    if not isinstance(dimension, int):
      raise TypeError('shape dimensions must be integer. '
                      'shape: {}, key: {}'.format(shape, key))
    if dimension < 1:
      raise ValueError('shape dimensions must be greater than 0. '
                       'shape: {}, key: {}'.format(shape, key))
  return shape


def _is_shape_and_default_value_compatible(default_value, shape):
  """Verifies compatibility of shape and default_value."""
  # Invalid condition:
  #  * if default_value is not a scalar and shape is empty
  #  * or if default_value is an iterable and shape is not empty
  if nest.is_sequence(default_value) != bool(shape):
    return False
  if not shape:
    return True
  if len(default_value) != shape[0]:
    return False
  for i in range(shape[0]):
    if not _is_shape_and_default_value_compatible(default_value[i], shape[1:]):
      return False
  return True


def _check_default_value(shape, default_value, dtype, key):
  """Returns default value as tuple if it's valid, otherwise raises errors.

  This function verifies that `default_value` is compatible with both `shape`
  and `dtype`. If it is not compatible, it raises an error. If it is compatible,
  it casts default_value to a tuple and returns it. `key` is used only
  for error message.

  Args:
    shape: An iterable of integers specifies the shape of the `Tensor`.
    default_value: If a single value is provided, the same value will be applied
      as the default value for every item. If an iterable of values is
      provided, the shape of the `default_value` should be equal to the given
      `shape`.
    dtype: defines the type of values. Default value is `tf.float32`. Must be a
      non-quantized, real integer or floating point type.
    key: Column name, used only for error messages.

  Returns:
    A tuple which will be used as default value.

  Raises:
    TypeError: if `default_value` is an iterable but not compatible with `shape`
    TypeError: if `default_value` is not compatible with `dtype`.
    ValueError: if `dtype` is not convertible to `tf.float32`.
  """
  if default_value is None:
    return None

  if isinstance(default_value, int):
    return _create_tuple(shape, default_value)

  if isinstance(default_value, float) and dtype.is_floating:
    return _create_tuple(shape, default_value)

  if callable(getattr(default_value, 'tolist', None)):  # Handles numpy arrays
    default_value = default_value.tolist()

  if nest.is_sequence(default_value):
    if not _is_shape_and_default_value_compatible(default_value, shape):
      raise ValueError(
          'The shape of default_value must be equal to given shape. '
          'default_value: {}, shape: {}, key: {}'.format(
              default_value, shape, key))
    # Check if the values in the list are all integers or are convertible to
    # floats.
    is_list_all_int = all(
        isinstance(v, int) for v in nest.flatten(default_value))
    is_list_has_float = any(
        isinstance(v, float) for v in nest.flatten(default_value))
    if is_list_all_int:
      return _as_tuple(default_value)
    if is_list_has_float and dtype.is_floating:
      return _as_tuple(default_value)
  raise TypeError('default_value must be compatible with dtype. '
                  'default_value: {}, dtype: {}, key: {}'.format(
                      default_value, dtype, key))


class HashedCategoricalColumn(
    CategoricalColumn,
    collections.namedtuple('HashedCategoricalColumn',
                           ('key', 'hash_bucket_size', 'dtype'))):
  """see `categorical_column_with_hash_bucket`."""

  @property
  def name(self):
    """See `FeatureColumn` base class."""
    return self.key

  @property
  def parse_example_spec(self):
    """See `FeatureColumn` base class."""
    return {self.key: parsing_ops.VarLenFeature(self.dtype)}

  def transform_feature(self, transformation_cache, state_manager):
    """Hashes the values in the feature_column."""
    input_tensor = _to_sparse_input_and_drop_ignore_values(
        transformation_cache.get(self.key, state_manager))
    if not isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
      raise ValueError('SparseColumn input must be a SparseTensor.')

    _assert_string_or_int(
        input_tensor.dtype,
        prefix='column_name: {} input_tensor'.format(self.key))

    if self.dtype.is_integer != input_tensor.dtype.is_integer:
      raise ValueError(
          'Column dtype and SparseTensors dtype must be compatible. '
          'key: {}, column dtype: {}, tensor dtype: {}'.format(
              self.key, self.dtype, input_tensor.dtype))

    if self.dtype == dtypes.string:
      sparse_values = input_tensor.values
    else:
      sparse_values = string_ops.as_string(input_tensor.values)

    sparse_id_values = string_ops.string_to_hash_bucket_fast(
        sparse_values, self.hash_bucket_size, name='lookup')
    return sparse_tensor_lib.SparseTensor(
        input_tensor.indices, sparse_id_values, input_tensor.dense_shape)

  @property
  def num_buckets(self):
    """Returns number of buckets in this sparse feature."""
    return self.hash_bucket_size

  def get_sparse_tensors(self, transformation_cache, state_manager):
    """See `CategoricalColumn` base class."""
    return CategoricalColumn.IdWeightPair(
        transformation_cache.get(self, state_manager), None)


class VocabularyFileCategoricalColumn(
    CategoricalColumn,
    collections.namedtuple('VocabularyFileCategoricalColumn',
                           ('key', 'vocabulary_file', 'vocabulary_size',
                            'num_oov_buckets', 'dtype', 'default_value'))):
  """See `categorical_column_with_vocabulary_file`."""

  @property
  def name(self):
    """See `FeatureColumn` base class."""
    return self.key

  @property
  def parse_example_spec(self):
    """See `FeatureColumn` base class."""
    return {self.key: parsing_ops.VarLenFeature(self.dtype)}

  def transform_feature(self, transformation_cache, state_manager):
    """Creates a lookup table for the vocabulary."""
    input_tensor = _to_sparse_input_and_drop_ignore_values(
        transformation_cache.get(self.key, state_manager))

    if self.dtype.is_integer != input_tensor.dtype.is_integer:
      raise ValueError(
          'Column dtype and SparseTensors dtype must be compatible. '
          'key: {}, column dtype: {}, tensor dtype: {}'.format(
              self.key, self.dtype, input_tensor.dtype))

    _assert_string_or_int(
        input_tensor.dtype,
        prefix='column_name: {} input_tensor'.format(self.key))

    key_dtype = self.dtype
    if input_tensor.dtype.is_integer:
      # `index_table_from_file` requires 64-bit integer keys.
      key_dtype = dtypes.int64
      input_tensor = math_ops.to_int64(input_tensor)

    # TODO(rohanj): Use state manager to manage the index table creation.
    return lookup_ops.index_table_from_file(
        vocabulary_file=self.vocabulary_file,
        num_oov_buckets=self.num_oov_buckets,
        vocab_size=self.vocabulary_size,
        default_value=self.default_value,
        key_dtype=key_dtype,
        name='{}_lookup'.format(self.key)).lookup(input_tensor)

  @property
  def num_buckets(self):
    """Returns number of buckets in this sparse feature."""
    return self.vocabulary_size + self.num_oov_buckets

  def get_sparse_tensors(self, transformation_cache, state_manager):
    """See `CategoricalColumn` base class."""
    return CategoricalColumn.IdWeightPair(
        transformation_cache.get(self, state_manager), None)


class VocabularyListCategoricalColumn(
    CategoricalColumn,
    collections.namedtuple(
        'VocabularyListCategoricalColumn',
        ('key', 'vocabulary_list', 'dtype', 'default_value', 'num_oov_buckets'))
):
  """See `categorical_column_with_vocabulary_list`."""

  @property
  def name(self):
    """See `FeatureColumn` base class."""
    return self.key

  @property
  def parse_example_spec(self):
    """See `FeatureColumn` base class."""
    return {self.key: parsing_ops.VarLenFeature(self.dtype)}

  def transform_feature(self, transformation_cache, state_manager):
    """Creates a lookup table for the vocabulary list."""
    input_tensor = _to_sparse_input_and_drop_ignore_values(
        transformation_cache.get(self.key, state_manager))

    if self.dtype.is_integer != input_tensor.dtype.is_integer:
      raise ValueError(
          'Column dtype and SparseTensors dtype must be compatible. '
          'key: {}, column dtype: {}, tensor dtype: {}'.format(
              self.key, self.dtype, input_tensor.dtype))

    _assert_string_or_int(
        input_tensor.dtype,
        prefix='column_name: {} input_tensor'.format(self.key))

    key_dtype = self.dtype
    if input_tensor.dtype.is_integer:
      # `index_table_from_tensor` requires 64-bit integer keys.
      key_dtype = dtypes.int64
      input_tensor = math_ops.to_int64(input_tensor)

    # TODO(rohanj): Use state manager to manage the index table creation.
    return lookup_ops.index_table_from_tensor(
        vocabulary_list=tuple(self.vocabulary_list),
        default_value=self.default_value,
        num_oov_buckets=self.num_oov_buckets,
        dtype=key_dtype,
        name='{}_lookup'.format(self.key)).lookup(input_tensor)

  @property
  def num_buckets(self):
    """Returns number of buckets in this sparse feature."""
    return len(self.vocabulary_list) + self.num_oov_buckets

  def get_sparse_tensors(self, transformation_cache, state_manager):
    """See `CategoricalColumn` base class."""
    return CategoricalColumn.IdWeightPair(
        transformation_cache.get(self, state_manager), None)


class IdentityCategoricalColumn(
    CategoricalColumn,
    collections.namedtuple('IdentityCategoricalColumn',
                           ('key', 'number_buckets', 'default_value'))):

  """See `categorical_column_with_identity`."""

  @property
  def name(self):
    """See `FeatureColumn` base class."""
    return self.key

  @property
  def parse_example_spec(self):
    """See `FeatureColumn` base class."""
    return {self.key: parsing_ops.VarLenFeature(dtypes.int64)}

  def transform_feature(self, transformation_cache, state_manager):
    """Returns a SparseTensor with identity values."""
    input_tensor = _to_sparse_input_and_drop_ignore_values(
        transformation_cache.get(self.key, state_manager))

    if not input_tensor.dtype.is_integer:
      raise ValueError(
          'Invalid input, not integer. key: {} dtype: {}'.format(
              self.key, input_tensor.dtype))

    values = math_ops.to_int64(input_tensor.values, name='values')
    num_buckets = math_ops.to_int64(self.num_buckets, name='num_buckets')
    zero = math_ops.to_int64(0, name='zero')
    if self.default_value is None:
      # Fail if values are out-of-range.
      assert_less = check_ops.assert_less(
          values, num_buckets, data=(values, num_buckets),
          name='assert_less_than_num_buckets')
      assert_greater = check_ops.assert_greater_equal(
          values, zero, data=(values,),
          name='assert_greater_or_equal_0')
      with ops.control_dependencies((assert_less, assert_greater)):
        values = array_ops.identity(values)
    else:
      # Assign default for out-of-range values.
      values = array_ops.where(
          math_ops.logical_or(
              values < zero, values >= num_buckets, name='out_of_range'),
          array_ops.fill(
              dims=array_ops.shape(values),
              value=math_ops.to_int64(self.default_value),
              name='default_values'),
          values)

    return sparse_tensor_lib.SparseTensor(
        indices=input_tensor.indices,
        values=values,
        dense_shape=input_tensor.dense_shape)

  @property
  def num_buckets(self):
    """Returns number of buckets in this sparse feature."""
    return self.number_buckets

  def get_sparse_tensors(self, transformation_cache, state_manager):
    """See `CategoricalColumn` base class."""
    return CategoricalColumn.IdWeightPair(
        transformation_cache.get(self, state_manager), None)


class WeightedCategoricalColumn(
    CategoricalColumn,
    collections.namedtuple(
        'WeightedCategoricalColumn',
        ('categorical_column', 'weight_feature_key', 'dtype'))):
  """See `weighted_categorical_column`."""

  @property
  def name(self):
    """See `FeatureColumn` base class."""
    return '{}_weighted_by_{}'.format(
        self.categorical_column.name, self.weight_feature_key)

  @property
  def parse_example_spec(self):
    """See `FeatureColumn` base class."""
    config = self.categorical_column.parse_example_spec
    if self.weight_feature_key in config:
      raise ValueError('Parse config {} already exists for {}.'.format(
          config[self.weight_feature_key], self.weight_feature_key))
    config[self.weight_feature_key] = parsing_ops.VarLenFeature(self.dtype)
    return config

  @property
  def num_buckets(self):
    """See `DenseColumn` base class."""
    return self.categorical_column.num_buckets

  def transform_feature(self, transformation_cache, state_manager):
    """Applies weights to tensor generated from `categorical_column`'."""
    weight_tensor = transformation_cache.get(self.weight_feature_key,
                                             state_manager)
    if weight_tensor is None:
      raise ValueError('Missing weights {}.'.format(self.weight_feature_key))
    weight_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
        weight_tensor)
    if self.dtype != weight_tensor.dtype.base_dtype:
      raise ValueError('Bad dtype, expected {}, but got {}.'.format(
          self.dtype, weight_tensor.dtype))
    if not isinstance(weight_tensor, sparse_tensor_lib.SparseTensor):
      # The weight tensor can be a regular Tensor. In this case, sparsify it.
      weight_tensor = _to_sparse_input_and_drop_ignore_values(
          weight_tensor, ignore_value=0.0)
    if not weight_tensor.dtype.is_floating:
      weight_tensor = math_ops.to_float(weight_tensor)
    return (transformation_cache.get(self.categorical_column, state_manager),
            weight_tensor)

  def get_sparse_tensors(self, transformation_cache, state_manager):
    """See `CategoricalColumn` base class."""
    tensors = transformation_cache.get(self, state_manager)
    return CategoricalColumn.IdWeightPair(tensors[0], tensors[1])


class CrossedColumn(
    CategoricalColumn,
    collections.namedtuple('CrossedColumn',
                           ('keys', 'hash_bucket_size', 'hash_key'))):
  """See `crossed_column`."""

  @property
  def name(self):
    """See `FeatureColumn` base class."""
    feature_names = []
    for key in _collect_leaf_level_keys(self):
      if isinstance(key, FeatureColumn):
        feature_names.append(key.name)
      else:  # key must be a string
        feature_names.append(key)
    return '_X_'.join(sorted(feature_names))

  @property
  def parse_example_spec(self):
    """See `FeatureColumn` base class."""
    config = {}
    for key in self.keys:
      if isinstance(key, FeatureColumn):
        config.update(key.parse_example_spec)
      else:  # key must be a string
        config.update({key: parsing_ops.VarLenFeature(dtypes.string)})
    return config

  def transform_feature(self, transformation_cache, state_manager):
    """Generates a hashed sparse cross from the input tensors."""
    feature_tensors = []
    for key in _collect_leaf_level_keys(self):
      if isinstance(key, six.string_types):
        feature_tensors.append(transformation_cache.get(key, state_manager))
      elif isinstance(key, CategoricalColumn):
        ids_and_weights = key.get_sparse_tensors(transformation_cache,
                                                 state_manager)
        if ids_and_weights.weight_tensor is not None:
          raise ValueError(
              'crossed_column does not support weight_tensor, but the given '
              'column populates weight_tensor. '
              'Given column: {}'.format(key.name))
        feature_tensors.append(ids_and_weights.id_tensor)
      else:
        raise ValueError('Unsupported column type. Given: {}'.format(key))
    return sparse_ops.sparse_cross_hashed(
        inputs=feature_tensors,
        num_buckets=self.hash_bucket_size,
        hash_key=self.hash_key)

  @property
  def num_buckets(self):
    """Returns number of buckets in this sparse feature."""
    return self.hash_bucket_size

  def get_sparse_tensors(self, transformation_cache, state_manager):
    """See `CategoricalColumn` base class."""
    return CategoricalColumn.IdWeightPair(
        transformation_cache.get(self, state_manager), None)


def _collect_leaf_level_keys(cross):
  """Collects base keys by expanding all nested crosses.

  Args:
    cross: A `CrossedColumn`.

  Returns:
    A list of strings or `CategoricalColumn` instances.
  """
  leaf_level_keys = []
  for k in cross.keys:
    if isinstance(k, CrossedColumn):
      leaf_level_keys.extend(_collect_leaf_level_keys(k))
    else:
      leaf_level_keys.append(k)
  return leaf_level_keys


# TODO(zakaria): Move this to embedding_ops and make it public.
def _safe_embedding_lookup_sparse(embedding_weights,
                                  sparse_ids,
                                  sparse_weights=None,
                                  combiner='mean',
                                  default_id=None,
                                  name=None,
                                  partition_strategy='div',
                                  max_norm=None):
  """Lookup embedding results, accounting for invalid IDs and empty features.

  The partitioned embedding in `embedding_weights` must all be the same shape
  except for the first dimension. The first dimension is allowed to vary as the
  vocabulary size is not necessarily a multiple of `P`.  `embedding_weights`
  may be a `PartitionedVariable` as returned by using `tf.get_variable()` with a
  partitioner.

  Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
  with non-positive weight. For an entry with no features, the embedding vector
  for `default_id` is returned, or the 0-vector if `default_id` is not supplied.

  The ids and weights may be multi-dimensional. Embeddings are always aggregated
  along the last dimension.

  Args:
    embedding_weights:  A list of `P` float `Tensor`s or values representing
        partitioned embedding `Tensor`s.  Alternatively, a `PartitionedVariable`
        created by partitioning along dimension 0.  The total unpartitioned
        shape should be `[e_0, e_1, ..., e_m]`, where `e_0` represents the
        vocab size and `e_1, ..., e_m` are the embedding dimensions.
    sparse_ids: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
        ids. `d_0` is typically batch size.
    sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing
        float weights corresponding to `sparse_ids`, or `None` if all weights
        are be assumed to be 1.0.
    combiner: A string specifying how to combine embedding results for each
        entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
        the default.
    default_id: The id to use for an entry with no features.
    name: A name for this operation (optional).
    partition_strategy: A string specifying the partitioning strategy.
        Currently `"div"` and `"mod"` are supported. Default is `"div"`.
    max_norm: If not `None`, all embeddings are l2-normalized to max_norm before
        combining.


  Returns:
    Dense `Tensor` of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.

  Raises:
    ValueError: if `embedding_weights` is empty.
  """
  if embedding_weights is None:
    raise ValueError('Missing embedding_weights %s.' % embedding_weights)
  if isinstance(embedding_weights, variables.PartitionedVariable):
    embedding_weights = list(embedding_weights)  # get underlying Variables.
  if not isinstance(embedding_weights, list):
    embedding_weights = [embedding_weights]
  if len(embedding_weights) < 1:
    raise ValueError('Missing embedding_weights %s.' % embedding_weights)

  dtype = sparse_weights.dtype if sparse_weights is not None else None
  # TODO(rohanj): Look into removing this convert_to_tensor call.
  embedding_weights = [
      ops.convert_to_tensor(w, dtype=dtype) for w in embedding_weights
  ]

  with ops.name_scope(name, 'embedding_lookup',
                      embedding_weights + [sparse_ids,
                                           sparse_weights]) as scope:
    # Reshape higher-rank sparse ids and weights to linear segment ids.
    original_shape = sparse_ids.dense_shape
    original_rank_dim = sparse_ids.dense_shape.get_shape()[0]
    original_rank = (
        array_ops.size(original_shape)
        if original_rank_dim.value is None
        else original_rank_dim.value)
    sparse_ids = sparse_ops.sparse_reshape(sparse_ids, [
        math_ops.reduce_prod(
            array_ops.slice(original_shape, [0], [original_rank - 1])),
        array_ops.gather(original_shape, original_rank - 1)])
    if sparse_weights is not None:
      sparse_weights = sparse_tensor_lib.SparseTensor(
          sparse_ids.indices,
          sparse_weights.values, sparse_ids.dense_shape)

    # Prune invalid ids and weights.
    sparse_ids, sparse_weights = _prune_invalid_ids(sparse_ids, sparse_weights)
    if combiner != 'sum':
      sparse_ids, sparse_weights = _prune_invalid_weights(
          sparse_ids, sparse_weights)

    # Fill in dummy values for empty features, if necessary.
    sparse_ids, is_row_empty = sparse_ops.sparse_fill_empty_rows(sparse_ids,
                                                                 default_id or
                                                                 0)
    if sparse_weights is not None:
      sparse_weights, _ = sparse_ops.sparse_fill_empty_rows(sparse_weights, 1.0)

    result = embedding_ops.embedding_lookup_sparse(
        embedding_weights,
        sparse_ids,
        sparse_weights,
        combiner=combiner,
        partition_strategy=partition_strategy,
        name=None if default_id is None else scope,
        max_norm=max_norm)

    if default_id is None:
      # Broadcast is_row_empty to the same shape as embedding_lookup_result,
      # for use in Select.
      is_row_empty = array_ops.tile(
          array_ops.reshape(is_row_empty, [-1, 1]),
          array_ops.stack([1, array_ops.shape(result)[1]]))

      result = array_ops.where(is_row_empty,
                               array_ops.zeros_like(result),
                               result,
                               name=scope)

    # Reshape back from linear ids back into higher-dimensional dense result.
    final_result = array_ops.reshape(
        result,
        array_ops.concat([
            array_ops.slice(
                math_ops.cast(original_shape, dtypes.int32), [0],
                [original_rank - 1]),
            array_ops.slice(array_ops.shape(result), [1], [-1])
        ], 0))
    final_result.set_shape(tensor_shape.unknown_shape(
        (original_rank_dim - 1).value).concatenate(result.get_shape()[1:]))
    return final_result


def _prune_invalid_ids(sparse_ids, sparse_weights):
  """Prune invalid IDs (< 0) from the input ids and weights."""
  is_id_valid = math_ops.greater_equal(sparse_ids.values, 0)
  if sparse_weights is not None:
    is_id_valid = math_ops.logical_and(
        is_id_valid,
        array_ops.ones_like(sparse_weights.values, dtype=dtypes.bool))
  sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_id_valid)
  if sparse_weights is not None:
    sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_id_valid)
  return sparse_ids, sparse_weights


def _prune_invalid_weights(sparse_ids, sparse_weights):
  """Prune invalid weights (< 0) from the input ids and weights."""
  if sparse_weights is not None:
    is_weights_valid = math_ops.greater(sparse_weights.values, 0)
    sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_weights_valid)
    sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_weights_valid)
  return sparse_ids, sparse_weights


class IndicatorColumn(DenseColumn, SequenceDenseColumn,
                      collections.namedtuple('IndicatorColumn',
                                             ('categorical_column'))):
  """Represents a one-hot column for use in deep networks.

  Args:
    categorical_column: A `CategoricalColumn` which is created by
      `categorical_column_with_*` function.
  """

  @property
  def name(self):
    """See `FeatureColumn` base class."""
    return '{}_indicator'.format(self.categorical_column.name)

  def transform_feature(self, transformation_cache, state_manager):
    """Returns dense `Tensor` representing feature.

    Args:
      transformation_cache: A `FeatureTransformationCache` object to access
        features.
      state_manager: A `StateManager` to create / access resources such as
        lookup tables.

    Returns:
      Transformed feature `Tensor`.

    Raises:
      ValueError: if input rank is not known at graph building time.
    """
    id_weight_pair = self.categorical_column.get_sparse_tensors(
        transformation_cache, state_manager)
    id_tensor = id_weight_pair.id_tensor
    weight_tensor = id_weight_pair.weight_tensor

    # If the underlying column is weighted, return the input as a dense tensor.
    if weight_tensor is not None:
      weighted_column = sparse_ops.sparse_merge(
          sp_ids=id_tensor,
          sp_values=weight_tensor,
          vocab_size=int(self.variable_shape[-1]))
      # Remove (?, -1) index
      weighted_column = sparse_ops.sparse_slice(weighted_column, [0, 0],
                                                weighted_column.dense_shape)
      return sparse_ops.sparse_tensor_to_dense(weighted_column)

    dense_id_tensor = sparse_ops.sparse_tensor_to_dense(
        id_tensor, default_value=-1)

    # One hot must be float for tf.concat reasons since all other inputs to
    # input_layer are float32.
    one_hot_id_tensor = array_ops.one_hot(
        dense_id_tensor,
        depth=self.variable_shape[-1],
        on_value=1.0,
        off_value=0.0)

    # Reduce to get a multi-hot per example.
    return math_ops.reduce_sum(one_hot_id_tensor, axis=[-2])

  @property
  def parse_example_spec(self):
    """See `FeatureColumn` base class."""
    return self.categorical_column.parse_example_spec

  @property
  def variable_shape(self):
    """Returns a `TensorShape` representing the shape of the dense `Tensor`."""
    return tensor_shape.TensorShape([1, self.categorical_column.num_buckets])

  def get_dense_tensor(self, transformation_cache, state_manager):
    """Returns dense `Tensor` representing feature.

    Args:
      transformation_cache: A `FeatureTransformationCache` object to access
        features.
      state_manager: A `StateManager` to create / access resources such as
        lookup tables.

    Returns:
      Dense `Tensor` created within `transform_feature`.

    Raises:
      ValueError: If `categorical_column` is a `SequenceCategoricalColumn`.
    """
    if isinstance(self.categorical_column, SequenceCategoricalColumn):
      raise ValueError(
          'In indicator_column: {}. '
          'categorical_column must not be of type SequenceCategoricalColumn. '
          'Suggested fix A: If you wish to use input_layer, use a '
          'non-sequence categorical_column_with_*. '
          'Suggested fix B: If you wish to create sequence input, use '
          'sequence_input_layer instead of input_layer. '
          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
                                       self.categorical_column))
    # Feature has been already transformed. Return the intermediate
    # representation created by transform_feature.
    return transformation_cache.get(self, state_manager)

  def get_sequence_dense_tensor(self, transformation_cache, state_manager):
    """See `SequenceDenseColumn` base class."""
    if not isinstance(self.categorical_column, SequenceCategoricalColumn):
      raise ValueError(
          'In indicator_column: {}. '
          'categorical_column must be of type SequenceCategoricalColumn '
          'to use sequence_input_layer. '
          'Suggested fix: Use one of sequence_categorical_column_with_*. '
          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
                                       self.categorical_column))
    # Feature has been already transformed. Return the intermediate
    # representation created by transform_feature.
    dense_tensor = transformation_cache.get(self, state_manager)
    sparse_tensors = self.categorical_column.get_sparse_tensors(
        transformation_cache, state_manager)
    sequence_length = _sequence_length_from_sparse_tensor(
        sparse_tensors.id_tensor)
    return SequenceDenseColumn.TensorSequenceLengthPair(
        dense_tensor=dense_tensor, sequence_length=sequence_length)


def _verify_static_batch_size_equality(tensors, columns):
  # bath_size is a tf.Dimension object.
  expected_batch_size = None
  for i in range(0, len(tensors)):
    if tensors[i].shape[0].value is not None:
      if expected_batch_size is None:
        bath_size_column_index = i
        expected_batch_size = tensors[i].shape[0]
      elif not expected_batch_size.is_compatible_with(tensors[i].shape[0]):
        raise ValueError(
            'Batch size (first dimension) of each feature must be same. '
            'Batch size of columns ({}, {}): ({}, {})'.format(
                columns[bath_size_column_index].name, columns[i].name,
                expected_batch_size, tensors[i].shape[0]))


def _sequence_length_from_sparse_tensor(sp_tensor, num_elements=1):
  """Returns a [batch_size] Tensor with per-example sequence length."""
  with ops.name_scope(None, 'sequence_length') as name_scope:
    row_ids = sp_tensor.indices[:, 0]
    column_ids = sp_tensor.indices[:, 1]
    column_ids += array_ops.ones_like(column_ids)
    seq_length = math_ops.to_int64(
        math_ops.segment_max(column_ids, segment_ids=row_ids) / num_elements)
    # If the last n rows do not have ids, seq_length will have shape
    # [batch_size - n]. Pad the remaining values with zeros.
    n_pad = array_ops.shape(sp_tensor)[:1] - array_ops.shape(seq_length)[:1]
    padding = array_ops.zeros(n_pad, dtype=seq_length.dtype)
    return array_ops.concat([seq_length, padding], axis=0, name=name_scope)


class SequenceCategoricalColumn(FeatureColumn,
                                collections.namedtuple(
                                    'SequenceCategoricalColumn',
                                    ('categorical_column'))):
  """Represents sequences of categorical data."""

  @property
  def name(self):
    """See `FeatureColumn` base class."""
    return self.categorical_column.name

  @property
  def parse_example_spec(self):
    """See `FeatureColumn` base class."""
    return self.categorical_column.parse_example_spec

  def transform_feature(self, transformation_cache, state_manager):
    """See `FeatureColumn` base class."""
    return self.categorical_column.transform_feature(transformation_cache,
                                                     state_manager)

  @property
  def num_buckets(self):
    """Returns number of buckets in this sparse feature."""
    return self.categorical_column.num_buckets

  def get_sequence_sparse_tensors(self, transformation_cache, state_manager):
    """Returns an IdWeightPair.

    `IdWeightPair` is a pair of `SparseTensor`s which represents ids and
    weights.

    `IdWeightPair.id_tensor` is typically a `batch_size` x `num_buckets`
    `SparseTensor` of `int64`. `IdWeightPair.weight_tensor` is either a
    `SparseTensor` of `float` or `None` to indicate all weights should be
    taken to be 1. If specified, `weight_tensor` must have exactly the same
    shape and indices as `sp_ids`. Expected `SparseTensor` is same as parsing
    output of a `VarLenFeature` which is a ragged matrix.

    Args:
      transformation_cache: A `FeatureTransformationCache` object to access
        features.
      state_manager: A `StateManager` to create / access resources such as
        lookup tables.
    """
    sparse_tensors = self.categorical_column.get_sparse_tensors(
        transformation_cache, state_manager)
    id_tensor = sparse_tensors.id_tensor
    weight_tensor = sparse_tensors.weight_tensor
    # Expands final dimension, so that embeddings are not combined during
    # embedding lookup.
    check_id_rank = check_ops.assert_equal(
        array_ops.rank(id_tensor), 2,
        data=[
            'Column {} expected ID tensor of rank 2. '.format(self.name),
            'id_tensor shape: ', array_ops.shape(id_tensor)])
    with ops.control_dependencies([check_id_rank]):
      id_tensor = sparse_ops.sparse_reshape(
          id_tensor,
          shape=array_ops.concat([id_tensor.dense_shape, [1]], axis=0))
    if weight_tensor is not None:
      check_weight_rank = check_ops.assert_equal(
          array_ops.rank(weight_tensor), 2,
          data=[
              'Column {} expected weight tensor of rank 2.'.format(self.name),
              'weight_tensor shape:', array_ops.shape(weight_tensor)])
      with ops.control_dependencies([check_weight_rank]):
        weight_tensor = sparse_ops.sparse_reshape(
            weight_tensor,
            shape=array_ops.concat([weight_tensor.dense_shape, [1]], axis=0))
    return CategoricalColumn.IdWeightPair(id_tensor, weight_tensor)