class_docstr: Tokenizes UTF-8 by splitting when there is a change in Unicode script.
  
  By default, this tokenizer leaves out scripts matching the whitespace unicode
  property (use the `keep_whitespace` argument to keep it), so in this case the
  results are similar to the `WhitespaceTokenizer`. Any punctuation
  will get its own token (since it is in a different script), and any script
  change in the input string will be the location of a split.
  
  Example:
  >>> tokenizer = tf_text.UnicodeScriptTokenizer()
  >>> tokens = tokenizer.tokenize(["xy.,z de", "fg?h", "abαβ"])
  >>> print(tokens.to_list())
  [[b'xy', b'.,', b'z', b'de'], [b'fg', b'?', b'h'],
   [b'ab', b'\xce\xb1\xce\xb2']]
  
  >>> tokens = tokenizer.tokenize(u"累計7239人")
  >>> print(tokens)
  tf.Tensor([b'\xe7\xb4\xaf\xe8\xa8\x88' b'7239' b'\xe4\xba\xba'], shape=(3,),
            dtype=string)
  
  Both the punctuation and the whitespace in the first string have been split,
  but the punctuation run is present as a token while the whitespace isn't
  emitted (by default). The third example shows the case of a script change
  without any whitespace. This results in a split at that boundary point.
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docstr: Initializes a new instance.
  
  Args:
    keep_whitespace: A boolean that specifices whether to emit whitespace
        tokens (default `False`).
docstr_tok: ["Initializes","a","new","instance",".","Args",":","keep_whitespace",":","A","boolean","that","specifices","whether","to","emit","whitespace","tokens","(","default","`","False","`",")","."]
code: def __init__(self, keep_whitespace=False):
      """Initializes a new instance.
  
      Args:
        keep_whitespace: A boolean that specifices whether to emit whitespace
            tokens (default `False`).
      """
      super(UnicodeScriptTokenizer, self).__init__()
      _tf_text_unicode_script_tokenizer_create_counter.get_cell().increase_by(
          1
      )
      self._keep_whitespace = keep_whitespace
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensorflow.python.eager import monitoring
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import string_ops
  from tensorflow.python.ops.ragged import ragged_conversion_ops
  from tensorflow.python.ops.ragged import ragged_string_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensor
  from tensorflow_text.python.ops.tokenization import TokenizerWithOffsets
  from tensorflow.python.framework import load_library
  from tensorflow.python.platform import resource_loader
label_desc: Use image node_id 1 to create a new UnicodeScriptTokenizer object from inherited base classes: TokenizerWithOffsets with example: obj = UnicodeScriptTokenizer(keep_whitespace)