parameters: (
      key,
      shape=(1,),
      default_value=0.,
      dtype=dtypes.float32,
      normalizer_fn=None)
return_statement: return SequenceNumericColumn(
        key,
        shape=shape,
        default_value=default_value,
        dtype=dtype,
        normalizer_fn=normalizer_fn)
docstring: Returns a feature column that represents sequences of numeric data.
  
    Example:
  
    ```python
    temperature = sequence_numeric_column('temperature')
    columns = [temperature]
  
    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 features.
      shape: The shape of the input data per sequence id. E.g. if `shape=(2,)`,
        each example must contain `2 * sequence_length` values.
      default_value: A single value compatible with `dtype` that is used for
        padding the sparse data into a dense `Tensor`.
      dtype: The type of values.
      normalizer_fn: If not `None`, a function that can be used to normalize the
        value of the tensor after `default_value` is applied for parsing.
        Normalizer function takes the input `Tensor` as its argument, and returns
        the output `Tensor`. (e.g. lambda x: (x - 3.0) / 4.2). Please note that
        even though the most common use case of this function is normalization, it
        can be used for any kind of Tensorflow transformations.
  
    Returns:
      A `SequenceNumericColumn`.
  
    Raises:
      TypeError: if any dimension in shape is not an int.
      ValueError: if any dimension in shape is not a positive integer.
      ValueError: if `dtype` is not convertible to `tf.float32`.
docstring_summary: Returns a feature column that represents sequences of numeric data.
docstring_tokens: Returns a feature column that represents sequences of numeric data .
function: def sequence_numeric_column(
      key,
      shape=(1,),
      default_value=0.,
      dtype=dtypes.float32,
      normalizer_fn=None):
    """Returns a feature column that represents sequences of numeric data.
  
    Example:
  
    ```python
    temperature = sequence_numeric_column('temperature')
    columns = [temperature]
  
    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 features.
      shape: The shape of the input data per sequence id. E.g. if `shape=(2,)`,
        each example must contain `2 * sequence_length` values.
      default_value: A single value compatible with `dtype` that is used for
        padding the sparse data into a dense `Tensor`.
      dtype: The type of values.
      normalizer_fn: If not `None`, a function that can be used to normalize the
        value of the tensor after `default_value` is applied for parsing.
        Normalizer function takes the input `Tensor` as its argument, and returns
        the output `Tensor`. (e.g. lambda x: (x - 3.0) / 4.2). Please note that
        even though the most common use case of this function is normalization, it
        can be used for any kind of Tensorflow transformations.
  
    Returns:
      A `SequenceNumericColumn`.
  
    Raises:
      TypeError: if any dimension in shape is not an int.
      ValueError: if any dimension in shape is not a positive integer.
      ValueError: if `dtype` is not convertible to `tf.float32`.
    """
    shape = fc._check_shape(shape=shape, key=key)
    if not (dtype.is_integer or dtype.is_floating):
      raise ValueError('dtype must be convertible to float. '
                       'dtype: {}, key: {}'.format(dtype, key))
    if normalizer_fn is not None and not callable(normalizer_fn):
      raise TypeError(
          'normalizer_fn must be a callable. Given: {}'.format(normalizer_fn))
  
    return SequenceNumericColumn(
        key,
        shape=shape,
        default_value=default_value,
        dtype=dtype,
        normalizer_fn=normalizer_fn)
function_tokens: def sequence_numeric_column ( key , shape = ( 1 , ) , default_value = 0. , dtype = dtypes . float32 , normalizer_fn = None ) : shape = fc . _check_shape ( shape = shape , key = key ) if not ( dtype . is_integer or dtype . is_floating ) : raise ValueError ( 'dtype must be convertible to float. ' 'dtype: {}, key: {}' . format ( dtype , key ) ) if normalizer_fn is not None and not callable ( normalizer_fn ) : raise TypeError ( 'normalizer_fn must be a callable. Given: {}' . format ( normalizer_fn ) ) return SequenceNumericColumn ( key , shape = shape , default_value = default_value , dtype = dtype , normalizer_fn = normalizer_fn )