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: Decodes the given serialized TF-example.
  Args:
    serialized_example: a serialized TF-example tensor.
    items: the list of items to decode. These must be a subset of the item
      keys in self._items_to_handlers. If `items` is left as None, then all
      of the items in self._items_to_handlers are decoded.
  Returns:
    the decoded items, a list of tensor.
docstr_tok: ["Decodes","the","given","serialized","TF-example",".","Args",":","serialized_example",":","a","serialized","TF-example","tensor",".","items",":","the","list","of","items","to","decode",".","These","must","be","a","subset","of","the","item","keys","in","self._items_to_handlers",".","If","`","items","`","is","left","as","None",",","then","all","of","the","items","in","self._items_to_handlers","are","decoded",".","Returns",":","the","decoded","items",",","a","list","of","tensor","."]
code: def decode(self, serialized_example, items=None):
      """Decodes the given serialized TF-example.
      Args:
        serialized_example: a serialized TF-example tensor.
        items: the list of items to decode. These must be a subset of the item
          keys in self._items_to_handlers. If `items` is left as None, then all
          of the items in self._items_to_handlers are decoded.
      Returns:
        the decoded items, a list of tensor.
      """
      context, sequence = tf.parse_single_sequence_example(
          serialized_example,
          self._context_keys_to_features,
          self._sequence_keys_to_features,
      )
  
      # Merge context and sequence features
      example = {}
      example.update(context)
      example.update(sequence)
  
      all_features = {}
      all_features.update(self._context_keys_to_features)
      all_features.update(self._sequence_keys_to_features)
  
      # Reshape non-sparse elements just once:
      for k, value in all_features.items():
          if isinstance(value, tf.FixedLenFeature):
              example[k] = tf.reshape(example[k], value.shape)
  
      if not items:
          items = self._items_to_handlers.keys()
  
      outputs = []
      for item in items:
          handler = self._items_to_handlers[item]
          keys_to_tensors = {key: example[key] for key in handler.keys}
          outputs.append(handler.tensors_to_item(keys_to_tensors))
      return outputs
  
imports: import tensorflow
  from tensorflow.contrib.slim.python.slim.data import data_decoder
label_desc: Use image node_id 3 for calling the TFSEquenceExampleDecoder obj's underlying member method code with example usage: obj.decode(serialized_example, items) and returns: outputs