code: def flatten(structure, expand_composites=False):
      """Returns a flat list from a given structure.
  
      Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
      for the definition of a structure.
  
      If the structure is an atom, then returns a single-item list: [structure].
  
      This is the inverse of the `nest.pack_sequence_as` method that takes in a
      flattened list and re-packs it into the nested structure.
  
      In the case of dict instances, the sequence consists of the values, sorted by
      key to ensure deterministic behavior. 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 `nest.pack_sequence_as`. This
      correctly repacks dicts and OrderedDicts 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.
  
      Users must not modify any collections used in nest while this function is
      running.
  
      Examples:
  
      1. Python dict (ordered by key):
  
        >>> dict = { "key3": "value3", "key1": "value1", "key2": "value2" }
        >>> tf.nest.flatten(dict)
        ['value1', 'value2', 'value3']
  
      2. For a nested python tuple:
  
        >>> tuple = ((1.0, 2.0), (3.0, 4.0, 5.0), 6.0)
        >>> tf.nest.flatten(tuple)
            [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
  
      3. For a nested dictionary of dictionaries:
  
        >>> dict = { "key3": {"c": (1.0, 2.0), "a": (3.0)},
        ... "key1": {"m": "val1", "g": "val2"} }
        >>> tf.nest.flatten(dict)
        ['val2', 'val1', 3.0, 1.0, 2.0]
  
      4. Numpy array (will not flatten):
  
        >>> array = np.array([[1, 2], [3, 4]])
        >>> tf.nest.flatten(array)
            [array([[1, 2],
                    [3, 4]])]
  
      5. `tf.Tensor` (will not flatten):
  
        >>> tensor = tf.constant([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]])
        >>> tf.nest.flatten(tensor)
            [<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
              array([[1., 2., 3.],
                     [4., 5., 6.],
                     [7., 8., 9.]], 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 just return the RaggedTensor as is.
  
        >>> tensor = tf.ragged.constant([[3, 1, 4, 1], [], [5, 9, 2]])
        >>> tf.nest.flatten(tensor, expand_composites=False)
        [<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2]]>]
  
      with `expand_composites=True`, we return the component Tensors that make up
      the RaggedTensor representation (the values and row_splits tensors)
  
        >>> tensor = tf.ragged.constant([[3, 1, 4, 1], [], [5, 9, 2]])
        >>> tf.nest.flatten(tensor, expand_composites=True)
        [<tf.Tensor: shape=(7,), dtype=int32, numpy=array([3, 1, 4, 1, 5, 9, 2],
                                                          dtype=int32)>,
         <tf.Tensor: shape=(4,), dtype=int64, numpy=array([0, 4, 4, 7])>]
  
      Args:
        structure: an atom or a nested structure. Note, numpy arrays are considered
          atoms and are not flattened.
        expand_composites: If true, then composite tensors such as
          `tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
          component tensors.
  
      Returns:
        A Python list, the flattened version of the input.
  
      Raises:
        TypeError: The nest is or contains a dict with non-sortable keys.
      """
      return nest_util.flatten(
          nest_util.Modality.CORE, structure, 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 9 for calling a global function with example usage: flatten(structure, expand_composites) and returns: nest_util