parameters: (
      key, hash_bucket_size, dtype=dtypes.string)
return_statement: return fc.SequenceCategoricalColumn(
        fc.categorical_column_with_hash_bucket(
            key=key,
            hash_bucket_size=hash_bucket_size,
            dtype=dtype))
docstring: A sequence of categorical terms where ids are set by hashing.
  
    Pass this to `embedding_column` or `indicator_column` to convert sequence
    categorical data into dense representation for input to sequence NN, such as
    RNN.
  
    Example:
  
    ```python
    tokens = sequence_categorical_column_with_hash_bucket(
        'tokens', hash_bucket_size=1000)
    tokens_embedding = embedding_column(tokens, dimension=10)
    columns = [tokens_embedding]
  
    features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
    sequence_feature_layer = SequenceFeatures(columns)
    sequence_input, sequence_length = sequence_feature_layer(features)
    sequence_length_mask = tf.sequence_mask(sequence_length)
  
    rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size)
    rnn_layer = tf.keras.layers.RNN(rnn_cell)
    outputs, state = rnn_layer(sequence_input, mask=sequence_length_mask)
    ```
  
    Args:
      key: A unique string identifying the input feature.
      hash_bucket_size: An int > 1. The number of buckets.
      dtype: The type of features. Only string and integer types are supported.
  
    Returns:
      A `SequenceCategoricalColumn`.
  
    Raises:
      ValueError: `hash_bucket_size` is not greater than 1.
      ValueError: `dtype` is neither string nor integer.
docstring_summary: A sequence of categorical terms where ids are set by hashing.
docstring_tokens: A sequence of categorical terms where ids are set by hashing .
function: def sequence_categorical_column_with_hash_bucket(
      key, hash_bucket_size, dtype=dtypes.string):
    """A sequence of categorical terms where ids are set by hashing.
  
    Pass this to `embedding_column` or `indicator_column` to convert sequence
    categorical data into dense representation for input to sequence NN, such as
    RNN.
  
    Example:
  
    ```python
    tokens = sequence_categorical_column_with_hash_bucket(
        'tokens', hash_bucket_size=1000)
    tokens_embedding = embedding_column(tokens, dimension=10)
    columns = [tokens_embedding]
  
    features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
    sequence_feature_layer = SequenceFeatures(columns)
    sequence_input, sequence_length = sequence_feature_layer(features)
    sequence_length_mask = tf.sequence_mask(sequence_length)
  
    rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size)
    rnn_layer = tf.keras.layers.RNN(rnn_cell)
    outputs, state = rnn_layer(sequence_input, mask=sequence_length_mask)
    ```
  
    Args:
      key: A unique string identifying the input feature.
      hash_bucket_size: An int > 1. The number of buckets.
      dtype: The type of features. Only string and integer types are supported.
  
    Returns:
      A `SequenceCategoricalColumn`.
  
    Raises:
      ValueError: `hash_bucket_size` is not greater than 1.
      ValueError: `dtype` is neither string nor integer.
    """
    return fc.SequenceCategoricalColumn(
        fc.categorical_column_with_hash_bucket(
            key=key,
            hash_bucket_size=hash_bucket_size,
            dtype=dtype))
function_tokens: def sequence_categorical_column_with_hash_bucket ( key , hash_bucket_size , dtype = dtypes . string ) : return fc . SequenceCategoricalColumn ( fc . categorical_column_with_hash_bucket ( key = key , hash_bucket_size = hash_bucket_size , dtype = dtype ) )