code: def ragged_tensor_to_string(rt, summarize=None):
      """Returns a scalar string tensor with the contents of a RaggedTensor.
  
      Requires that `rt.shape.rank` is not `None`.
  
      Note: this converts the entire `RaggedTensor` into a single string scalar.
      If you want to convert individual elements, use `tf.strings.as_string(rt)`.
  
      >>> rt1 = tf.ragged.constant([[1, 2, 3], [4, 5]])
      >>> ragged_tensor_to_string(rt1).numpy()
      b'[[1, 2, 3], [4, 5]]'
  
      >>> rt2 = tf.ragged.constant([[['a'], ['b', 'c']], [['d', 'e', 'f'], []]])
      >>> ragged_tensor_to_string(rt2).numpy()
      b"[[['a'], ['b', 'c']], [['d', 'e', 'f'], []]]"
  
      >>> rt3 = tf.ragged.constant([[1], [2, 3, 4, 5, 6], [], [], [7], [8, 9]])
      >>> ragged_tensor_to_string(rt3, summarize=2).numpy()
      b'[[1], [2, 3, ..., 5, 6], ..., [7], [8, 9]]'
  
      Args:
        rt: The RaggedTensor that should be converted to a string.
        summarize: If specified, then only the first and last `summarize` elements
          within each dimension are included in the string. If `-1` or `None`, then
          all elements are included.
      """
      if (
          summarize is not None
          and summarize != -1
          and not (isinstance(summarize, int) and summarize > 0)
      ):
          raise ValueError(
              "Expected summarize to be -1 or a positive int, got %r"
              % summarize
          )
      with ops.name_scope(None, "AsString", [rt]):
          rt = ragged_tensor.convert_to_tensor_or_ragged_tensor(rt)
          if rt.shape.rank is None:
              raise ValueError(
                  "RaggedTensor to_string requires that rt.shape.rank "
                  "is not None."
              )
          # Convert all elements of `rt` to strings.
          if rt.dtype == dtypes.string:
              escaped = string_ops.regex_replace(
                  rt.flat_values, r"(['\\])", r"\\\1"
              )
              str_t = rt.with_flat_values("'" + escaped + "'")
          else:
              str_t = rt.with_flat_values(
                  string_ops.as_string(rt.flat_values)
              )
  
          return _ragged_tensor_to_string(str_t, summarize)
  
imports: import typing
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import gen_string_ops
  from tensorflow.python.ops import map_fn
  from tensorflow.python.ops import string_ops
  from tensorflow.python.ops.ragged import ragged_array_ops
  from tensorflow.python.ops.ragged import ragged_functional_ops
  from tensorflow.python.ops.ragged import ragged_math_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.util import compat
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 14 for calling a global function with example usage: ragged_tensor_to_string(rt, summarize) and returns: _ragged_tensor_to_string