code: 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
          )
      )
  
imports: import collections
  from tensorflow.python.feature_column import feature_column_v2
  from tensorflow.python.feature_column import serialization
  from tensorflow.python.feature_column import utils
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import parsing_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
  from tensorflow.tools.docs import doc_controls
label_desc: Use image node_id 3 for calling a global function with example usage: sequence_categorical_column_with_hash_bucket(key, hash_bucket_size, dtype) and returns: fc