code: def unbatch():
      """Splits elements of a dataset into multiple elements on the batch dimension.
  
      For example, if elements of the dataset are shaped `[B, a0, a1, ...]`,
      where `B` may vary for each input element, then for each element in the
      dataset, the unbatched dataset will contain `B` consecutive elements
      of shape `[a0, a1, ...]`.
  
      ```python
      # NOTE: The following example uses `{ ... }` to represent the contents
      # of a dataset.
      a = { ['a', 'b', 'c'], ['a', 'b'], ['a', 'b', 'c', 'd'] }
  
      a.unbatch() == {
          'a', 'b', 'c', 'a', 'b', 'a', 'b', 'c', 'd'}
      ```
  
      Returns:
        A `Dataset` transformation function, which can be passed to
        `tf.data.Dataset.apply`.
      """
  
      def _apply_fn(dataset):
          return dataset.unbatch()
  
      return _apply_fn
  
imports: from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import structured_function
  from tensorflow.python.data.util import convert
  from tensorflow.python.data.util import nest
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import gen_experimental_dataset_ops
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 5 for calling a global function with example usage: unbatch() and returns: _apply_fn, dataset