class_docstr: `RegexSplitter` splits text on the given regular expression.
  
  The default is a newline character pattern. It can also return the beginning
  and ending byte offsets as well.
  
  By default, this splitter will break on newlines, ignoring any trailing ones.
  >>> splitter = RegexSplitter()
  >>> text_input=[
  ...       b"Hi there.\nWhat time is it?\nIt is gametime.",
  ...       b"Who let the dogs out?\nWho?\nWho?\nWho?\n\n",
  ...   ]
  >>> splitter.split(text_input)
  <tf.RaggedTensor [[b'Hi there.', b'What time is it?', b'It is gametime.'],
                    [b'Who let the dogs out?', b'Who?', b'Who?', b'Who?']]>
  
  The splitter can be passed a custom split pattern, as well. The pattern
  can be any string, but we're using a single character (tab) in this example.
  >>> splitter = RegexSplitter(split_regex='\t')
  >>> text_input=[
  ...       b"Hi there.\tWhat time is it?\tIt is gametime.",
  ...       b"Who let the dogs out?\tWho?\tWho?\tWho?\t\t",
  ...   ]
  >>> splitter.split(text_input)
  <tf.RaggedTensor [[b'Hi there.', b'What time is it?', b'It is gametime.'],
                    [b'Who let the dogs out?', b'Who?', b'Who?', b'Who?']]>
code: def split_with_offsets(
      self, input
  ):  # pylint: disable=redefined-builtin
      return regex_split_with_offsets(input, self._split_regex)
  
file_path: text/tensorflow_text/python/ops/regex_split_ops.py
imports: from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.framework import load_library
  from tensorflow.python.platform import resource_loader
  from tensorflow_text.python.ops import splitter
label_desc: Use image node_id 3 for calling the RegexSplitter obj's underlying member method code with example usage: obj.split_with_offsets(input) and returns: regex_split_with_offsets