parameters: (key,
                                          hash_bucket_size,
                                          dtype=dtypes.string)
return_statement: return HashedCategoricalColumn(key, hash_bucket_size, dtype)
docstring: 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
    import tensorflow as tf
    keywords = tf.feature_column.categorical_column_with_hash_bucket("keywords",
    10000)
    columns = [keywords]
    features = {'keywords': tf.constant([['Tensorflow', 'Keras', 'RNN', 'LSTM',
    'CNN'], ['LSTM', 'CNN', 'Tensorflow', 'Keras', 'RNN'], ['CNN', 'Tensorflow',
    'LSTM', 'Keras', 'RNN']])}
    linear_prediction, _, _ = tf.compat.v1.feature_column.linear_model(features,
    columns)
  
    # or
    import tensorflow as tf
    keywords = tf.feature_column.categorical_column_with_hash_bucket("keywords",
    10000)
    keywords_embedded = tf.feature_column.embedding_column(keywords, 16)
    columns = [keywords_embedded]
    features = {'keywords': tf.constant([['Tensorflow', 'Keras', 'RNN', 'LSTM',
    'CNN'], ['LSTM', 'CNN', 'Tensorflow', 'Keras', 'RNN'], ['CNN', 'Tensorflow',
    'LSTM', 'Keras', 'RNN']])}
    input_layer = tf.keras.layers.DenseFeatures(columns)
    dense_tensor = input_layer(features)
    ```
  
    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.
docstring_summary: Represents sparse feature where ids are set by hashing.
docstring_tokens: Represents sparse feature where ids are set by hashing .
function: 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
    import tensorflow as tf
    keywords = tf.feature_column.categorical_column_with_hash_bucket("keywords",
    10000)
    columns = [keywords]
    features = {'keywords': tf.constant([['Tensorflow', 'Keras', 'RNN', 'LSTM',
    'CNN'], ['LSTM', 'CNN', 'Tensorflow', 'Keras', 'RNN'], ['CNN', 'Tensorflow',
    'LSTM', 'Keras', 'RNN']])}
    linear_prediction, _, _ = tf.compat.v1.feature_column.linear_model(features,
    columns)
  
    # or
    import tensorflow as tf
    keywords = tf.feature_column.categorical_column_with_hash_bucket("keywords",
    10000)
    keywords_embedded = tf.feature_column.embedding_column(keywords, 16)
    columns = [keywords_embedded]
    features = {'keywords': tf.constant([['Tensorflow', 'Keras', 'RNN', 'LSTM',
    'CNN'], ['LSTM', 'CNN', 'Tensorflow', 'Keras', 'RNN'], ['CNN', 'Tensorflow',
    'LSTM', 'Keras', 'RNN']])}
    input_layer = tf.keras.layers.DenseFeatures(columns)
    dense_tensor = input_layer(features)
    ```
  
    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))
  
    fc_utils.assert_key_is_string(key)
    fc_utils.assert_string_or_int(dtype, prefix='column_name: {}'.format(key))
  
    return HashedCategoricalColumn(key, hash_bucket_size, dtype)
function_tokens: def categorical_column_with_hash_bucket ( key , hash_bucket_size , dtype = dtypes . string ) : 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 ) ) fc_utils . assert_key_is_string ( key ) fc_utils . assert_string_or_int ( dtype , prefix = 'column_name: {}' . format ( key ) ) return HashedCategoricalColumn ( key , hash_bucket_size , dtype )