parameters: (key,
                                               vocabulary_list,
                                               dtype=None,
                                               default_value=-1,
                                               num_oov_buckets=0)
return_statement: return _VocabularyListCategoricalColumn(
        key=key, vocabulary_list=tuple(vocabulary_list), dtype=dtype,
        default_value=default_value, num_oov_buckets=num_oov_buckets)
docstring: 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.io.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.io.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.io.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.
docstring_summary: A `_CategoricalColumn` with in-memory vocabulary.
docstring_tokens: A _CategoricalColumn with in - memory vocabulary .
function: 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.io.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.io.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.io.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))
    fc_utils.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))
    fc_utils.assert_string_or_int(dtype, prefix='column_name: {}'.format(key))
    fc_utils.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)
function_tokens: def _categorical_column_with_vocabulary_list ( key , vocabulary_list , dtype = None , default_value = - 1 , num_oov_buckets = 0 ) : 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 ) ) fc_utils . 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 ) ) fc_utils . assert_string_or_int ( dtype , prefix = 'column_name: {}' . format ( key ) ) fc_utils . 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 )