class_docstr: A decoder for TensorFlow Examples.
  Decoding Example proto buffers is comprised of two stages: (1) Example parsing
  and (2) tensor manipulation.
  In the first stage, the tf.parse_example function is called with a list of
  FixedLenFeatures and SparseLenFeatures. These instances tell TF how to parse
  the example. The output of this stage is a set of tensors.
  In the second stage, the resulting tensors are manipulated to provide the
  requested 'item' tensors.
  To perform this decoding operation, an ExampleDecoder is given a list of
  ItemHandlers. Each ItemHandler indicates the set of features for stage 1 and
  contains the instructions for post_processing its tensors for stage 2.
docstr: Constructs the decoder.
  Args:
    keys_to_features: a dictionary from TF-Example keys to either
      tf.VarLenFeature or tf.FixedLenFeature instances. See tensorflow's
      parsing_ops.py.
    items_to_handlers: a dictionary from items (strings) to ItemHandler
      instances. Note that the ItemHandler's are provided the keys that they
      use to return the final item Tensors.
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code: def __init__(
      self,
      context_keys_to_features,
      sequence_keys_to_features,
      items_to_handlers,
  ):
      """Constructs the decoder.
      Args:
        keys_to_features: a dictionary from TF-Example keys to either
          tf.VarLenFeature or tf.FixedLenFeature instances. See tensorflow's
          parsing_ops.py.
        items_to_handlers: a dictionary from items (strings) to ItemHandler
          instances. Note that the ItemHandler's are provided the keys that they
          use to return the final item Tensors.
      """
      self._context_keys_to_features = context_keys_to_features
      self._sequence_keys_to_features = sequence_keys_to_features
      self._items_to_handlers = items_to_handlers
  
imports: import tensorflow
  from tensorflow.contrib.slim.python.slim.data import data_decoder
label_desc: Use image node_id 1 to create a new TFSEquenceExampleDecoder object from inherited base classes: data_decoder with example: obj = TFSEquenceExampleDecoder(context_keys_to_features, sequence_keys_to_features, items_to_handlers)