code: def pack_sequence_as(
      structure, flat_sequence, expand_composites=False
  ):
      """Returns a given flattened sequence packed into a given structure.
  
      Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
      for the definition of a structure.
  
      If `structure` is an atom, `flat_sequence` must be a single-item list;
      in this case the return value is `flat_sequence[0]`.
  
      If `structure` is or contains a dict instance, the keys will be sorted to
      pack the flat sequence in deterministic order. This is true also for
      `OrderedDict` instances: their sequence order is ignored, the sorting order of
      keys is used instead. The same convention is followed in `flatten`.
      This correctly repacks dicts and `OrderedDict`s after they have been
      flattened, and also allows flattening an `OrderedDict` and then repacking it
      back using a corresponding plain dict, or vice-versa.
      Dictionaries with non-sortable keys cannot be flattened.
  
      Examples:
  
      1. Python dict:
  
        >>> structure = { "key3": "", "key1": "", "key2": "" }
        >>> flat_sequence = ["value1", "value2", "value3"]
        >>> tf.nest.pack_sequence_as(structure, flat_sequence)
        {'key3': 'value3', 'key1': 'value1', 'key2': 'value2'}
  
      2. For a nested python tuple:
  
        >>> structure = (('a','b'), ('c','d','e'), 'f')
        >>> flat_sequence = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
        >>> tf.nest.pack_sequence_as(structure, flat_sequence)
        ((1.0, 2.0), (3.0, 4.0, 5.0), 6.0)
  
      3. For a nested dictionary of dictionaries:
  
        >>> structure = { "key3": {"c": ('alpha', 'beta'), "a": ('gamma')},
        ...               "key1": {"e": "val1", "d": "val2"} }
        >>> flat_sequence = ['val2', 'val1', 3.0, 1.0, 2.0]
        >>> tf.nest.pack_sequence_as(structure, flat_sequence)
        {'key3': {'c': (1.0, 2.0), 'a': 3.0}, 'key1': {'e': 'val1', 'd': 'val2'}}
  
      4. Numpy array (considered a scalar):
  
        >>> structure = ['a']
        >>> flat_sequence = [np.array([[1, 2], [3, 4]])]
        >>> tf.nest.pack_sequence_as(structure, flat_sequence)
        [array([[1, 2],
               [3, 4]])]
  
      5. tf.Tensor (considered a scalar):
  
        >>> structure = ['a']
        >>> flat_sequence = [tf.constant([[1., 2., 3.], [4., 5., 6.]])]
        >>> tf.nest.pack_sequence_as(structure, flat_sequence)
        [<tf.Tensor: shape=(2, 3), dtype=float32,
         numpy= array([[1., 2., 3.], [4., 5., 6.]], dtype=float32)>]
  
      6. `tf.RaggedTensor`: This is a composite tensor thats representation consists
      of a flattened list of 'values' and a list of 'row_splits' which indicate how
      to chop up the flattened list into different rows. For more details on
      `tf.RaggedTensor`, please visit
      https://www.tensorflow.org/api_docs/python/tf/RaggedTensor.
  
      With `expand_composites=False`, we treat RaggedTensor as a scalar.
  
        >>> structure = { "foo": tf.ragged.constant([[1, 2], [3]]),
        ...               "bar": tf.constant([[5]]) }
        >>> flat_sequence = [ "one", "two" ]
        >>> tf.nest.pack_sequence_as(structure, flat_sequence,
        ... expand_composites=False)
        {'foo': 'two', 'bar': 'one'}
  
      With `expand_composites=True`, we expect that the flattened input contains
      the tensors making up the ragged tensor i.e. the values and row_splits
      tensors.
  
        >>> structure = { "foo": tf.ragged.constant([[1., 2.], [3.]]),
        ...               "bar": tf.constant([[5.]]) }
        >>> tensors = tf.nest.flatten(structure, expand_composites=True)
        >>> print(tensors)
        [<tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[5.]],
         dtype=float32)>,
         <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 2., 3.],
         dtype=float32)>,
         <tf.Tensor: shape=(3,), dtype=int64, numpy=array([0, 2, 3])>]
        >>> verified_tensors = [tf.debugging.check_numerics(t, 'invalid tensor: ')
        ...                     if t.dtype==tf.float32 else t
        ...                     for t in tensors]
        >>> tf.nest.pack_sequence_as(structure, verified_tensors,
        ...                          expand_composites=True)
        {'foo': <tf.RaggedTensor [[1.0, 2.0], [3.0]]>,
         'bar': <tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[5.]],
         dtype=float32)>}
  
      Args:
        structure: Nested structure, whose structure is given by nested lists,
          tuples, and dicts. Note: numpy arrays and strings are considered
          scalars.
        flat_sequence: flat sequence to pack.
        expand_composites: If true, then composite tensors such as
          `tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
          component tensors.
  
      Returns:
        packed: `flat_sequence` converted to have the same recursive structure as
          `structure`.
  
      Raises:
        ValueError: If `flat_sequence` and `structure` have different
          atom counts.
        TypeError: `structure` is or contains a dict with non-sortable keys.
      """
      return nest_util.pack_sequence_as(
          nest_util.Modality.CORE,
          structure,
          flat_sequence,
          expand_composites,
      )
  
imports: from tensorflow.python.util import _pywrap_nest
  from tensorflow.python.util import _pywrap_utils
  from tensorflow.python.util import nest_util
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 12 for calling a global function with example usage: pack_sequence_as(structure, flat_sequence, expand_composites) and returns: nest_util