parameters: (
      key, num_buckets, default_value=None)
return_statement: return fc._SequenceCategoricalColumn(
        fc._categorical_column_with_identity(
            key=key, num_buckets=num_buckets, default_value=default_value))
docstring: Returns a feature column that represents sequences of integers.
  
    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
    watches = sequence_categorical_column_with_identity(
        'watches', num_buckets=1000)
    watches_embedding = embedding_column(watches, dimension=10)
    columns = [watches_embedding]
  
    features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
    input_layer, sequence_length = sequence_input_layer(features, columns)
  
    rnn_cell = tf.compat.v1.nn.rnn_cell.BasicRNNCell(hidden_size)
    outputs, state = tf.compat.v1.nn.dynamic_rnn(
        rnn_cell, inputs=input_layer, sequence_length=sequence_length)
    ```
  
    Args:
      key: A unique string identifying the input feature.
      num_buckets: Range of inputs. Namely, inputs are expected to be in the
        range `[0, num_buckets)`.
      default_value: If `None`, this column's graph operations will fail for
        out-of-range inputs. Otherwise, this value must be in the range
        `[0, num_buckets)`, and will replace out-of-range inputs.
  
    Returns:
      A `_SequenceCategoricalColumn`.
  
    Raises:
      ValueError: if `num_buckets` is less than one.
      ValueError: if `default_value` is not in range `[0, num_buckets)`.
docstring_summary: Returns a feature column that represents sequences of integers.
docstring_tokens: Returns a feature column that represents sequences of integers .
function: def sequence_categorical_column_with_identity(
      key, num_buckets, default_value=None):
    """Returns a feature column that represents sequences of integers.
  
    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
    watches = sequence_categorical_column_with_identity(
        'watches', num_buckets=1000)
    watches_embedding = embedding_column(watches, dimension=10)
    columns = [watches_embedding]
  
    features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
    input_layer, sequence_length = sequence_input_layer(features, columns)
  
    rnn_cell = tf.compat.v1.nn.rnn_cell.BasicRNNCell(hidden_size)
    outputs, state = tf.compat.v1.nn.dynamic_rnn(
        rnn_cell, inputs=input_layer, sequence_length=sequence_length)
    ```
  
    Args:
      key: A unique string identifying the input feature.
      num_buckets: Range of inputs. Namely, inputs are expected to be in the
        range `[0, num_buckets)`.
      default_value: If `None`, this column's graph operations will fail for
        out-of-range inputs. Otherwise, this value must be in the range
        `[0, num_buckets)`, and will replace out-of-range inputs.
  
    Returns:
      A `_SequenceCategoricalColumn`.
  
    Raises:
      ValueError: if `num_buckets` is less than one.
      ValueError: if `default_value` is not in range `[0, num_buckets)`.
    """
    return fc._SequenceCategoricalColumn(
        fc._categorical_column_with_identity(
            key=key, num_buckets=num_buckets, default_value=default_value))
function_tokens: def sequence_categorical_column_with_identity ( key , num_buckets , default_value = None ) : return fc . _SequenceCategoricalColumn ( fc . _categorical_column_with_identity ( key = key , num_buckets = num_buckets , default_value = default_value ) )