code: def dense_to_sparse_batch(batch_size, row_shape):
      """A transformation that batches ragged elements into `tf.sparse.SparseTensor`s.
  
      Like `Dataset.padded_batch()`, this transformation combines multiple
      consecutive elements of the dataset, which might have different
      shapes, into a single element. The resulting element has three
      components (`indices`, `values`, and `dense_shape`), which
      comprise a `tf.sparse.SparseTensor` that represents the same data. The
      `row_shape` represents the dense shape of each row in the
      resulting `tf.sparse.SparseTensor`, to which the effective batch size is
      prepended. For example:
  
      ```python
      # NOTE: The following examples use `{ ... }` to represent the
      # contents of a dataset.
      a = { ['a', 'b', 'c'], ['a', 'b'], ['a', 'b', 'c', 'd'] }
  
      a.apply(tf.data.experimental.dense_to_sparse_batch(
          batch_size=2, row_shape=[6])) ==
      {
          ([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1]],  # indices
           ['a', 'b', 'c', 'a', 'b'],                 # values
           [2, 6]),                                   # dense_shape
          ([[0, 0], [0, 1], [0, 2], [0, 3]],
           ['a', 'b', 'c', 'd'],
           [1, 6])
      }
      ```
  
      Args:
        batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
          consecutive elements of this dataset to combine in a single batch.
        row_shape: A `tf.TensorShape` or `tf.int64` vector tensor-like object
          representing the equivalent dense shape of a row in the resulting
          `tf.sparse.SparseTensor`. Each element of this dataset must have the same
          rank as `row_shape`, and must have size less than or equal to `row_shape`
          in each dimension.
  
      Returns:
        A `Dataset` transformation function, which can be passed to
        `tf.data.Dataset.apply`.
      """
  
      def _apply_fn(dataset):
          return dataset.sparse_batch(batch_size, row_shape)
  
      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 2 for calling a global function with example usage: dense_to_sparse_batch(batch_size, row_shape) and returns: _apply_fn, dataset