text: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """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.feature_column import feature_column as fc_old
  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 import training
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.layers import base
  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 resource_variable_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 _InputLayerStateManager(StateManager):
    """Manages the state of InputLayer."""
  
    def __init__(self, layer, feature_columns, trainable):
      """Creates an _InputLayerStateManager object.
  
      Args:
        layer: The input layer this state manager is associated with.
        feature_columns: List of feature columns for the input layer
        trainable: Whether by default, variables created are trainable or not.
      """
      self._trainable = trainable
      self._layer = layer
      self._cols_to_vars_map = {}
      self._cols_to_names_map = {}
      for column in sorted(feature_columns, key=lambda x: x.name):
        self._cols_to_vars_map[column] = {}
        base_name = column.name
        if isinstance(column, SharedEmbeddingColumn):
          base_name = column.shared_collection_name
        with variable_scope.variable_scope(base_name) as vs:
          self._cols_to_names_map[column] = _strip_leading_slashes(vs.name)
  
    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.')
      with variable_scope.variable_scope(self._cols_to_names_map[feature_column]):
        var = self._layer.add_variable(
            name=name,
            shape=shape,
            dtype=dtype,
            initializer=initializer,
            trainable=self._trainable and trainable,
            # 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, ...]
    features = tf.parse_example(..., features=make_parse_example_spec(columns))
    feature_layer = FeatureLayer(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 = _InputLayerStateManager(self, self._feature_columns,
                                                    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.variable_scope(None, default_name=self.name):
            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):
        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 linear_model(features,
                   feature_columns,
                   units=1,
                   sparse_combiner='sum',
                   weight_collections=None,
                   trainable=True,
                   cols_to_vars=None):
    """Returns a linear prediction `Tensor` based on given `feature_columns`.
  
    This function 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: `linear_model` 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 ...]
    features = tf.parse_example(..., features=make_parse_example_spec(columns))
    prediction = linear_model(features, 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 are `Tensor` or `SparseTensor` depending on
        corresponding `_FeatureColumn`.
      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.
      weight_collections: A list of collection names to which the Variable will be
        added. Note that, variables will also be added to collections
        `tf.GraphKeys.GLOBAL_VARIABLES` and `ops.GraphKeys.MODEL_VARIABLES`.
      trainable: If `True` also add the variable to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
      cols_to_vars: If not `None`, must be a dictionary that will be filled with a
        mapping from `_FeatureColumn` to associated list of `Variable`s.  For
        example, after the call, we might have cols_to_vars = {
          _NumericColumn(
            key='numeric_feature1', shape=(1,):
          [<tf.Variable 'linear_model/price2/weights:0' shape=(1, 1)>],
          'bias': [<tf.Variable 'linear_model/bias_weights:0' shape=(1,)>],
          _NumericColumn(
            key='numeric_feature2', shape=(2,)):
          [<tf.Variable 'linear_model/price1/weights:0' shape=(2, 1)>]}
        If a column creates no variables, its value will be an empty list. Note
        that cols_to_vars will also contain a string key 'bias' that maps to a
        list of Variables.
  
    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 an item in `feature_columns` is neither a `_DenseColumn`
        nor `_CategoricalColumn`.
    """
    with variable_scope.variable_scope(None, 'linear_model') as vs:
      model_name = _strip_leading_slashes(vs.name)
    linear_model_layer = _LinearModel(
        feature_columns=feature_columns,
        units=units,
        sparse_combiner=sparse_combiner,
        weight_collections=weight_collections,
        trainable=trainable,
        name=model_name)
    retval = linear_model_layer(features)  # pylint: disable=not-callable
    if cols_to_vars is not None:
      cols_to_vars.update(linear_model_layer.cols_to_vars())
    return retval
  
  
  def _add_to_collections(var, weight_collections):
    """Adds a var to the list of weight_collections provided.
  
    Handles the case for partitioned and non-partitioned variables.
  
    Args:
      var: A variable or Partitioned Variable.
      weight_collections: List of collections to add variable to.
    """
    for weight_collection in weight_collections:
      # The layer self.add_variable call already adds it to GLOBAL_VARIABLES.
      if weight_collection == ops.GraphKeys.GLOBAL_VARIABLES:
        continue
      # TODO(rohanj): Explore adding a _get_variable_list method on `Variable`
      # so that we don't have to do this check.
      if isinstance(var, variables.PartitionedVariable):
        for constituent_var in list(var):
          ops.add_to_collection(weight_collection, constituent_var)
      else:
        ops.add_to_collection(weight_collection, var)
  
  
  class _FCLinearWrapper(base.Layer):
    """Wraps a _FeatureColumn in a layer for use in a linear model.
  
    See `linear_model` above.
    """
  
    def __init__(self,
                 feature_column,
                 units=1,
                 sparse_combiner='sum',
                 weight_collections=None,
                 trainable=True,
                 name=None,
                 **kwargs):
      super(_FCLinearWrapper, self).__init__(
          trainable=trainable, name=name, **kwargs)
      self._feature_column = feature_column
      self._units = units
      self._sparse_combiner = sparse_combiner
      self._weight_collections = weight_collections
  
    def build(self, _):
      if isinstance(self._feature_column, fc_old._CategoricalColumn):  # pylint: disable=protected-access
        weight = self.add_variable(
            name='weights',
            shape=(self._feature_column._num_buckets, self._units),  # pylint: disable=protected-access
            initializer=init_ops.zeros_initializer(),
            trainable=self.trainable)
      else:
        num_elements = self._feature_column._variable_shape.num_elements()  # pylint: disable=protected-access
        weight = self.add_variable(
            name='weights',
            shape=[num_elements, self._units],
            initializer=init_ops.zeros_initializer(),
            trainable=self.trainable)
      _add_to_collections(weight, self._weight_collections)
      self._weight_var = weight
      self.built = True
  
    def call(self, builder):
      weighted_sum = fc_old._create_weighted_sum(  # pylint: disable=protected-access
          column=self._feature_column,
          builder=builder,
          units=self._units,
          sparse_combiner=self._sparse_combiner,
          weight_collections=self._weight_collections,
          trainable=self.trainable,
          weight_var=self._weight_var)
      return weighted_sum
  
  
  class _BiasLayer(base.Layer):
    """A layer for the bias term.
    """
  
    def __init__(self,
                 units=1,
                 trainable=True,
                 weight_collections=None,
                 name=None,
                 **kwargs):
      super(_BiasLayer, self).__init__(trainable=trainable, name=name, **kwargs)
      self._units = units
      self._weight_collections = weight_collections
  
    def build(self, _):
      self._bias_variable = self.add_variable(
          'bias_weights',
          shape=[self._units],
          initializer=init_ops.zeros_initializer(),
          trainable=self.trainable)
      _add_to_collections(self._bias_variable, self._weight_collections)
      self.built = True
  
    def call(self, _):
      return self._bias_variable
  
  
  def _get_expanded_variable_list(var_list):
    returned_list = []
    for variable in var_list:
      if (isinstance(variable, variables.Variable) or
          resource_variable_ops.is_resource_variable(variable)):
        returned_list.append(variable)  # Single variable case.
      else:  # Must be a PartitionedVariable, so convert into a list.
        returned_list.extend(list(variable))
    return returned_list
  
  
  def _strip_leading_slashes(name):
    return name.rsplit('/', 1)[-1]
  
  
  class _LinearModel(training.Model):
    """Creates a linear model using feature columns.
  
    See `linear_model` for details.
    """
  
    def __init__(self,
                 feature_columns,
                 units=1,
                 sparse_combiner='sum',
                 weight_collections=None,
                 trainable=True,
                 name=None,
                 **kwargs):
      super(_LinearModel, self).__init__(name=name, **kwargs)
      self._feature_columns = fc_old._normalize_feature_columns(  # pylint: disable=protected-access
          feature_columns)
      self._weight_collections = list(weight_collections or [])
      if ops.GraphKeys.GLOBAL_VARIABLES not in self._weight_collections:
        self._weight_collections.append(ops.GraphKeys.GLOBAL_VARIABLES)
      if ops.GraphKeys.MODEL_VARIABLES not in self._weight_collections:
        self._weight_collections.append(ops.GraphKeys.MODEL_VARIABLES)
  
      column_layers = {}
      for column in sorted(self._feature_columns, key=lambda x: x.name):
        with variable_scope.variable_scope(
            None, default_name=column._var_scope_name) as vs:  # pylint: disable=protected-access
          # Having the fully expressed variable scope name ends up doubly
          # expressing the outer scope (scope with which this method was called)
          # in the name of the variable that would get created.
          column_name = _strip_leading_slashes(vs.name)
        column_layer = _FCLinearWrapper(column, units, sparse_combiner,
                                        self._weight_collections, trainable,
                                        column_name, **kwargs)
        column_layers[column_name] = column_layer
      self._column_layers = self._add_layers(column_layers)
      self._bias_layer = _BiasLayer(
          units=units,
          trainable=trainable,
          weight_collections=self._weight_collections,
          name='bias_layer',
          **kwargs)
      self._cols_to_vars = {}
  
    def cols_to_vars(self):
      """Returns a dict mapping _FeatureColumns to variables.
  
      See `linear_model` for more information.
      This is not populated till `call` is called i.e. layer is built.
      """
      return self._cols_to_vars
  
    def call(self, features):
      with variable_scope.variable_scope(self.name):
        for column in self._feature_columns:
          if not isinstance(
              column,
              (
                  fc_old._DenseColumn,  # pylint: disable=protected-access
                  fc_old._CategoricalColumn)):  # pylint: disable=protected-access
            raise ValueError(
                'Items of feature_columns must be either a '
                '_DenseColumn or _CategoricalColumn. Given: {}'.format(column))
        weighted_sums = []
        ordered_columns = []
        builder = fc_old._LazyBuilder(features)  # pylint: disable=protected-access
        for layer in sorted(self._column_layers.values(), key=lambda x: x.name):
          column = layer._feature_column  # pylint: disable=protected-access
          ordered_columns.append(column)
          weighted_sum = layer(builder)
          weighted_sums.append(weighted_sum)
          self._cols_to_vars[column] = ops.get_collection(
              ops.GraphKeys.GLOBAL_VARIABLES, scope=layer.scope_name)
  
        _verify_static_batch_size_equality(weighted_sums, ordered_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_layer(  # pylint: disable=not-callable
                builder,
                scope=variable_scope.get_variable_scope()),  # pylint: disable=not-callable
            name='weighted_sum')
        bias = self._bias_layer.variables[0]
        self._cols_to_vars['bias'] = _get_expanded_variable_list([bias])
      return predictions
  
    def _add_layers(self, layers):
      # "Magic" required for keras.Model classes to track all the variables in
      # a list of layers.Layer objects.
      # TODO(ashankar): Figure out API so user code doesn't have to do this.
      for name, layer in layers.items():
        setattr(self, 'layer-%s' % name, layer)
      return layers
  
  
  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 _create_weighted_sum(column,
                           transformation_cache,
                           state_manager,
                           units,
                           sparse_combiner,
                           weight_collections,
                           trainable,
                           weight_var=None):
    """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,
          units=units,
          sparse_combiner=sparse_combiner,
          weight_collections=weight_collections,
          trainable=trainable,
          weight_var=weight_var)
    else:
      return _create_dense_column_weighted_sum(
          column=column,
          transformation_cache=transformation_cache,
          state_manager=state_manager,
          units=units,
          weight_collections=weight_collections,
          trainable=trainable,
          weight_var=weight_var)
  
  
  def _create_dense_column_weighted_sum(column,
                                        transformation_cache,
                                        state_manager,
                                        units,
                                        weight_collections,
                                        trainable,
                                        weight_var=None):
    """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))
    if weight_var is not None:
      weight = weight_var
    else:
      weight = variable_scope.get_variable(
          name='weights',
          shape=[num_elements, units],
          initializer=init_ops.zeros_initializer(),
          trainable=trainable,
          collections=weight_collections)
    return math_ops.matmul(tensor, weight, 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,
                                              units,
                                              sparse_combiner,
                                              weight_collections,
                                              trainable,
                                              weight_var=None):
    # 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])
  
    if weight_var is not None:
      weight = weight_var
    else:
      weight = variable_scope.get_variable(
          name='weights',
          shape=(column.num_buckets, units),
          initializer=init_ops.zeros_initializer(),
          trainable=trainable,
          collections=weight_collections)
    return _safe_embedding_lookup_sparse(
        weight,
        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,
            # 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]
  
  
  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."""
      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
    if not isinstance(embedding_weights[0],
                      resource_variable_ops.ResourceVariable):
      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)
  