code: def sequence_categorical_column_with_vocabulary_list(
      key,
      vocabulary_list,
      dtype=None,
      default_value=-1,
      num_oov_buckets=0,
  ):
      """A sequence of categorical terms where ids use an in-memory list.
  
      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
      colors = sequence_categorical_column_with_vocabulary_list(
          key='colors', vocabulary_list=('R', 'G', 'B', 'Y'),
          num_oov_buckets=2)
      colors_embedding = embedding_column(colors, dimension=3)
      columns = [colors_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.
        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 `SequenceCategoricalColumn`.
  
      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.
      """
      return fc.SequenceCategoricalColumn(
          fc.categorical_column_with_vocabulary_list(
              key=key,
              vocabulary_list=vocabulary_list,
              dtype=dtype,
              default_value=default_value,
              num_oov_buckets=num_oov_buckets,
          )
      )
  
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 5 for calling a global function with example usage: sequence_categorical_column_with_vocabulary_list(key, vocabulary_list, dtype, default_value, num_oov_buckets) and returns: fc