parameters: (
      serialized, context_features=None, sequence_features=None,
      example_name=None, name=None)
return_statement: return _parse_single_sequence_example_raw(
        serialized, context_sparse_keys, context_sparse_types,
        context_dense_keys, context_dense_types, context_dense_defaults,
        context_dense_shapes, feature_list_sparse_keys,
        feature_list_sparse_types, feature_list_dense_keys,
        feature_list_dense_types, feature_list_dense_shapes,
        feature_list_dense_defaults, example_name, name)
docstring: Parses a single `SequenceExample` proto.
  
    Parses a single serialized [`SequenceExample`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto)
    proto given in `serialized`.
  
    This op parses a serialized sequence example into a tuple of dictionaries
    mapping keys to `Tensor` and `SparseTensor` objects respectively.
    The first dictionary contains mappings for keys appearing in
    `context_features`, and the second dictionary contains mappings for keys
    appearing in `sequence_features`.
  
    At least one of `context_features` and `sequence_features` must be provided
    and non-empty.
  
    The `context_features` keys are associated with a `SequenceExample` as a
    whole, independent of time / frame.  In contrast, the `sequence_features` keys
    provide a way to access variable-length data within the `FeatureList` section
    of the `SequenceExample` proto.  While the shapes of `context_features` values
    are fixed with respect to frame, the frame dimension (the first dimension)
    of `sequence_features` values may vary between `SequenceExample` protos,
    and even between `feature_list` keys within the same `SequenceExample`.
  
    `context_features` contains `VarLenFeature` and `FixedLenFeature` objects.
    Each `VarLenFeature` is mapped to a `SparseTensor`, and each `FixedLenFeature`
    is mapped to a `Tensor`, of the specified type, shape, and default value.
  
    `sequence_features` contains `VarLenFeature` and `FixedLenSequenceFeature`
    objects. Each `VarLenFeature` is mapped to a `SparseTensor`, and each
    `FixedLenSequenceFeature` is mapped to a `Tensor`, each of the specified type.
    The shape will be `(T,) + df.dense_shape` for `FixedLenSequenceFeature` `df`, where
    `T` is the length of the associated `FeatureList` in the `SequenceExample`.
    For instance, `FixedLenSequenceFeature([])` yields a scalar 1-D `Tensor` of
    static shape `[None]` and dynamic shape `[T]`, while
    `FixedLenSequenceFeature([k])` (for `int k >= 1`) yields a 2-D matrix `Tensor`
    of static shape `[None, k]` and dynamic shape `[T, k]`.
  
    Each `SparseTensor` corresponding to `sequence_features` represents a ragged
    vector.  Its indices are `[time, index]`, where `time` is the `FeatureList`
    entry and `index` is the value's index in the list of values associated with
    that time.
  
    `FixedLenFeature` entries with a `default_value` and `FixedLenSequenceFeature`
    entries with `allow_missing=True` are optional; otherwise, we will fail if
    that `Feature` or `FeatureList` is missing from any example in `serialized`.
  
    `example_name` may contain a descriptive name for the corresponding serialized
    proto. This may be useful for debugging purposes, but it has no effect on the
    output. If not `None`, `example_name` must be a scalar.
  
    Args:
      serialized: A scalar (0-D Tensor) of type string, a single binary
        serialized `SequenceExample` proto.
      context_features: A `dict` mapping feature keys to `FixedLenFeature` or
        `VarLenFeature` values. These features are associated with a
        `SequenceExample` as a whole.
      sequence_features: A `dict` mapping feature keys to
        `FixedLenSequenceFeature` or `VarLenFeature` values. These features are
        associated with data within the `FeatureList` section of the
        `SequenceExample` proto.
      example_name: A scalar (0-D Tensor) of strings (optional), the name of
        the serialized proto.
      name: A name for this operation (optional).
  
    Returns:
      A tuple of two `dict`s, each mapping keys to `Tensor`s and `SparseTensor`s.
      The first dict contains the context key/values.
      The second dict contains the feature_list key/values.
  
    Raises:
      ValueError: if any feature is invalid.
docstring_summary: Parses a single `SequenceExample` proto.
docstring_tokens: Parses a single SequenceExample proto .
function: def parse_single_sequence_example(
      serialized, context_features=None, sequence_features=None,
      example_name=None, name=None):
    # pylint: disable=line-too-long
    """Parses a single `SequenceExample` proto.
  
    Parses a single serialized [`SequenceExample`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto)
    proto given in `serialized`.
  
    This op parses a serialized sequence example into a tuple of dictionaries
    mapping keys to `Tensor` and `SparseTensor` objects respectively.
    The first dictionary contains mappings for keys appearing in
    `context_features`, and the second dictionary contains mappings for keys
    appearing in `sequence_features`.
  
    At least one of `context_features` and `sequence_features` must be provided
    and non-empty.
  
    The `context_features` keys are associated with a `SequenceExample` as a
    whole, independent of time / frame.  In contrast, the `sequence_features` keys
    provide a way to access variable-length data within the `FeatureList` section
    of the `SequenceExample` proto.  While the shapes of `context_features` values
    are fixed with respect to frame, the frame dimension (the first dimension)
    of `sequence_features` values may vary between `SequenceExample` protos,
    and even between `feature_list` keys within the same `SequenceExample`.
  
    `context_features` contains `VarLenFeature` and `FixedLenFeature` objects.
    Each `VarLenFeature` is mapped to a `SparseTensor`, and each `FixedLenFeature`
    is mapped to a `Tensor`, of the specified type, shape, and default value.
  
    `sequence_features` contains `VarLenFeature` and `FixedLenSequenceFeature`
    objects. Each `VarLenFeature` is mapped to a `SparseTensor`, and each
    `FixedLenSequenceFeature` is mapped to a `Tensor`, each of the specified type.
    The shape will be `(T,) + df.dense_shape` for `FixedLenSequenceFeature` `df`, where
    `T` is the length of the associated `FeatureList` in the `SequenceExample`.
    For instance, `FixedLenSequenceFeature([])` yields a scalar 1-D `Tensor` of
    static shape `[None]` and dynamic shape `[T]`, while
    `FixedLenSequenceFeature([k])` (for `int k >= 1`) yields a 2-D matrix `Tensor`
    of static shape `[None, k]` and dynamic shape `[T, k]`.
  
    Each `SparseTensor` corresponding to `sequence_features` represents a ragged
    vector.  Its indices are `[time, index]`, where `time` is the `FeatureList`
    entry and `index` is the value's index in the list of values associated with
    that time.
  
    `FixedLenFeature` entries with a `default_value` and `FixedLenSequenceFeature`
    entries with `allow_missing=True` are optional; otherwise, we will fail if
    that `Feature` or `FeatureList` is missing from any example in `serialized`.
  
    `example_name` may contain a descriptive name for the corresponding serialized
    proto. This may be useful for debugging purposes, but it has no effect on the
    output. If not `None`, `example_name` must be a scalar.
  
    Args:
      serialized: A scalar (0-D Tensor) of type string, a single binary
        serialized `SequenceExample` proto.
      context_features: A `dict` mapping feature keys to `FixedLenFeature` or
        `VarLenFeature` values. These features are associated with a
        `SequenceExample` as a whole.
      sequence_features: A `dict` mapping feature keys to
        `FixedLenSequenceFeature` or `VarLenFeature` values. These features are
        associated with data within the `FeatureList` section of the
        `SequenceExample` proto.
      example_name: A scalar (0-D Tensor) of strings (optional), the name of
        the serialized proto.
      name: A name for this operation (optional).
  
    Returns:
      A tuple of two `dict`s, each mapping keys to `Tensor`s and `SparseTensor`s.
      The first dict contains the context key/values.
      The second dict contains the feature_list key/values.
  
    Raises:
      ValueError: if any feature is invalid.
    """
    # pylint: enable=line-too-long
    if not (context_features or sequence_features):
      raise ValueError("Missing features.")
    (context_sparse_keys, context_sparse_types, context_dense_keys,
     context_dense_types, context_dense_defaults,
     context_dense_shapes) = _features_to_raw_params(
         context_features, [VarLenFeature, FixedLenFeature])
    (feature_list_sparse_keys, feature_list_sparse_types,
     feature_list_dense_keys, feature_list_dense_types,
     feature_list_dense_defaults,
     feature_list_dense_shapes) = _features_to_raw_params(
         sequence_features, [VarLenFeature, FixedLenSequenceFeature])
    return _parse_single_sequence_example_raw(
        serialized, context_sparse_keys, context_sparse_types,
        context_dense_keys, context_dense_types, context_dense_defaults,
        context_dense_shapes, feature_list_sparse_keys,
        feature_list_sparse_types, feature_list_dense_keys,
        feature_list_dense_types, feature_list_dense_shapes,
        feature_list_dense_defaults, example_name, name)
function_tokens: def parse_single_sequence_example ( serialized , context_features = None , sequence_features = None , example_name = None , name = None ) : # pylint: disable=line-too-long # pylint: enable=line-too-long if not ( context_features or sequence_features ) : raise ValueError ( "Missing features." ) ( context_sparse_keys , context_sparse_types , context_dense_keys , context_dense_types , context_dense_defaults , context_dense_shapes ) = _features_to_raw_params ( context_features , [ VarLenFeature , FixedLenFeature ] ) ( feature_list_sparse_keys , feature_list_sparse_types , feature_list_dense_keys , feature_list_dense_types , feature_list_dense_defaults , feature_list_dense_shapes ) = _features_to_raw_params ( sequence_features , [ VarLenFeature , FixedLenSequenceFeature ] ) return _parse_single_sequence_example_raw ( serialized , context_sparse_keys , context_sparse_types , context_dense_keys , context_dense_types , context_dense_defaults , context_dense_shapes , feature_list_sparse_keys , feature_list_sparse_types , feature_list_dense_keys , feature_list_dense_types , feature_list_dense_shapes , feature_list_dense_defaults , example_name , name )