parameters: (serialized, features, name=None, example_names=None)
return_statement: return _construct_sparse_tensors_for_sparse_features(features, outputs)
docstring: Parses a single `Example` proto.
  
    Similar to `parse_example`, except:
  
    For dense tensors, the returned `Tensor` is identical to the output of
    `parse_example`, except there is no batch dimension, the output shape is the
    same as the shape given in `dense_shape`.
  
    For `SparseTensor`s, the first (batch) column of the indices matrix is removed
    (the indices matrix is a column vector), the values vector is unchanged, and
    the first (`batch_size`) entry of the shape vector is removed (it is now a
    single element vector).
  
    One might see performance advantages by batching `Example` protos with
    `parse_example` instead of using this function directly.
  
    Args:
      serialized: A scalar string Tensor, a single serialized Example.
        See `_parse_single_example_raw` documentation for more details.
      features: A `dict` mapping feature keys to `FixedLenFeature` or
        `VarLenFeature` values.
      name: A name for this operation (optional).
      example_names: (Optional) A scalar string Tensor, the associated name.
        See `_parse_single_example_raw` documentation for more details.
  
    Returns:
      A `dict` mapping feature keys to `Tensor` and `SparseTensor` values.
  
    Raises:
      ValueError: if any feature is invalid.
docstring_summary: Parses a single `Example` proto.
docstring_tokens: Parses a single Example proto .
function: def parse_single_example(serialized, features, name=None, example_names=None):
    """Parses a single `Example` proto.
  
    Similar to `parse_example`, except:
  
    For dense tensors, the returned `Tensor` is identical to the output of
    `parse_example`, except there is no batch dimension, the output shape is the
    same as the shape given in `dense_shape`.
  
    For `SparseTensor`s, the first (batch) column of the indices matrix is removed
    (the indices matrix is a column vector), the values vector is unchanged, and
    the first (`batch_size`) entry of the shape vector is removed (it is now a
    single element vector).
  
    One might see performance advantages by batching `Example` protos with
    `parse_example` instead of using this function directly.
  
    Args:
      serialized: A scalar string Tensor, a single serialized Example.
        See `_parse_single_example_raw` documentation for more details.
      features: A `dict` mapping feature keys to `FixedLenFeature` or
        `VarLenFeature` values.
      name: A name for this operation (optional).
      example_names: (Optional) A scalar string Tensor, the associated name.
        See `_parse_single_example_raw` documentation for more details.
  
    Returns:
      A `dict` mapping feature keys to `Tensor` and `SparseTensor` values.
  
    Raises:
      ValueError: if any feature is invalid.
    """
    if not features:
      raise ValueError("Missing features.")
    features = _prepend_none_dimension(features)
    (sparse_keys, sparse_types, dense_keys, dense_types, dense_defaults,
     dense_shapes) = _features_to_raw_params(
         features,
         [VarLenFeature, FixedLenFeature, FixedLenSequenceFeature, SparseFeature])
    outputs = _parse_single_example_raw(
        serialized, example_names, sparse_keys, sparse_types, dense_keys,
        dense_types, dense_defaults, dense_shapes, name)
    return _construct_sparse_tensors_for_sparse_features(features, outputs)
function_tokens: def parse_single_example ( serialized , features , name = None , example_names = None ) : if not features : raise ValueError ( "Missing features." ) features = _prepend_none_dimension ( features ) ( sparse_keys , sparse_types , dense_keys , dense_types , dense_defaults , dense_shapes ) = _features_to_raw_params ( features , [ VarLenFeature , FixedLenFeature , FixedLenSequenceFeature , SparseFeature ] ) outputs = _parse_single_example_raw ( serialized , example_names , sparse_keys , sparse_types , dense_keys , dense_types , dense_defaults , dense_shapes , name ) return _construct_sparse_tensors_for_sparse_features ( features , outputs )