class_docstr: A `Dataset` that encodes dense inputs as ragged (w/ ragged_rank=0).
  
  In particular:
  
  * Any tf.Tensor elements with rank>0 are encoded as ragged tensors with
    ragged_rank=0.  This allows tensors with varying shape to be batched
    together.
  * Any other elements are left as-is.
class_docstr_tok: ["A","`","Dataset","`","that","encodes","dense","inputs","as","ragged","(","w\/","ragged_rank=0",")",".","In","particular",":","*","Any","tf.Tensor","elements","with","rank",">","0","are","encoded","as","ragged","tensors","with","ragged_rank=0",".","This","allows","tensors","with","varying","shape","to","be","batched","together",".","*","Any","other","elements","are","left","as-is","."]
code: def element_spec(self):
      return self._structure
  
file_path: tensorflow-upstream/tensorflow/python/data/ops/ragged_batch_op.py
imports: from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import structured_function
  from tensorflow.python.data.util import nest
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor
  from tensorflow.python.ops.ragged import ragged_tensor
label_desc: Use image node_id 2 for calling the _DenseToRaggedDataset obj's underlying member method code with example usage: obj.element_spec() and returns: self