code: def sequence_categorical_column_with_vocabulary_file(
      key,
      vocabulary_file,
      vocabulary_size=None,
      num_oov_buckets=0,
      default_value=None,
      dtype=dtypes.string,
  ):
      """A sequence of categorical terms where ids use a vocabulary file.
  
      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
      states = sequence_categorical_column_with_vocabulary_file(
          key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
          num_oov_buckets=5)
      states_embedding = embedding_column(states, dimension=10)
      columns = [states_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_file: The vocabulary file name.
        vocabulary_size: Number of the elements in the vocabulary. This must be no
          greater than length of `vocabulary_file`, if less than length, later
          values are ignored. If None, it is set to the length of `vocabulary_file`.
        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
          `[vocabulary_size, vocabulary_size+num_oov_buckets)` based on a hash of
          the input value. A positive `num_oov_buckets` can not be specified with
          `default_value`.
        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`.
        dtype: The type of features. Only string and integer types are supported.
  
      Returns:
        A `SequenceCategoricalColumn`.
  
      Raises:
        ValueError: `vocabulary_file` is missing or cannot be opened.
        ValueError: `vocabulary_size` is missing or < 1.
        ValueError: `num_oov_buckets` is a negative integer.
        ValueError: `num_oov_buckets` and `default_value` are both specified.
        ValueError: `dtype` is neither string nor integer.
      """
      return fc.SequenceCategoricalColumn(
          fc.categorical_column_with_vocabulary_file(
              key=key,
              vocabulary_file=vocabulary_file,
              vocabulary_size=vocabulary_size,
              num_oov_buckets=num_oov_buckets,
              default_value=default_value,
              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 4 for calling a global function with example usage: sequence_categorical_column_with_vocabulary_file(key, vocabulary_file, vocabulary_size, num_oov_buckets, default_value, dtype) and returns: fc