parameters: (key, num_buckets, default_value=None)
return_statement: return _IdentityCategoricalColumn(
        key=key, num_buckets=num_buckets, default_value=default_value)
docstring: 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. Note that these values are independent of the
    `default_value` argument.
  
    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)`.
docstring_summary: A `_CategoricalColumn` that returns identity values.
docstring_tokens: A _CategoricalColumn that returns identity values .
function: 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. Note that these values are independent of the
    `default_value` argument.
  
    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))
    return _IdentityCategoricalColumn(
        key=key, num_buckets=num_buckets, default_value=default_value)
function_tokens: def categorical_column_with_identity ( key , num_buckets , default_value = None ) : 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 ) ) return _IdentityCategoricalColumn ( key = key , num_buckets = num_buckets , default_value = default_value )