class_docstr: Tokenizes a tensor of UTF-8 strings on Unicode character boundaries.
  
  
  Resulting tokens are integers (unicode codepoints). Scalar input will
  produce a `Tensor` output containing the codepoints. Tensor inputs will
  produce `RaggedTensor` outputs.
  
  Example:
  
  >>> tokenizer = tf_text.UnicodeCharTokenizer()
  >>> tokens = tokenizer.tokenize("abc")
  >>> print(tokens)
  tf.Tensor([97 98 99], shape=(3,), dtype=int32)
  
  >>> tokens = tokenizer.tokenize(["abc", "de"])
  >>> print(tokens)
  <tf.RaggedTensor [[97, 98, 99], [100, 101]]>
  
  Note: any remaining illegal and special UTF-8 characters (like BOM
  characters) in the input string will not be treated specially by the tokenizer
  and show up in the output tokens. These should be normalized out before
  or after tokenization if they are unwanted in the application.
  
  >>> t = ["abc" + chr(0xfffe) + chr(0x1fffe) ]
  >>> tokens = tokenizer.tokenize(t)
  >>> print(tokens.to_list())
  [[97, 98, 99, 65534, 131070]]
  
  Passing malformed UTF-8 will result in unpredictable behavior. Make sure
  inputs conform to UTF-8.
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docstr: Detokenizes input codepoints (integers) to UTF-8 strings.
  
  Example:
  
  >>> tokenizer = tf_text.UnicodeCharTokenizer()
  >>> tokens = tokenizer.tokenize(["abc", "de"])
  >>> s = tokenizer.detokenize(tokens)
  >>> print(s)
  tf.Tensor([b'abc' b'de'], shape=(2,), dtype=string)
  
  Args:
    input: A `RaggedTensor` or `Tensor` of codepoints (ints) with a rank of at
      least 1.
    name: The name argument that is passed to the op function.
  
  Returns:
    A N-1 dimensional string tensor of the text corresponding to the UTF-8
    codepoints in the input.
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code: def detokenize(
      self, input, name=None
  ):  # pylint: disable=redefined-builtin
      """Detokenizes input codepoints (integers) to UTF-8 strings.
  
      Example:
  
      >>> tokenizer = tf_text.UnicodeCharTokenizer()
      >>> tokens = tokenizer.tokenize(["abc", "de"])
      >>> s = tokenizer.detokenize(tokens)
      >>> print(s)
      tf.Tensor([b'abc' b'de'], shape=(2,), dtype=string)
  
      Args:
        input: A `RaggedTensor` or `Tensor` of codepoints (ints) with a rank of at
          least 1.
        name: The name argument that is passed to the op function.
  
      Returns:
        A N-1 dimensional string tensor of the text corresponding to the UTF-8
        codepoints in the input.
      """
      name = None
      with ops.name_scope(name, "UnicodeCharTokenize", [input, self]):
          input_tensor = (
              ragged_tensor.convert_to_tensor_or_ragged_tensor(input)
          )
          return ragged_string_ops.unicode_encode(input_tensor, "UTF-8")
  
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 math_ops
  from tensorflow.python.ops import string_ops
  from tensorflow.python.ops.ragged import ragged_array_ops
  from tensorflow.python.ops.ragged import ragged_string_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow_text.python.ops.tokenization import Detokenizer
  from tensorflow_text.python.ops.tokenization import TokenizerWithOffsets
label_desc: Use image node_id 4 for calling the UnicodeCharTokenizer obj's underlying member method code with example usage: obj.detokenize(input, name) and returns: ragged_string_ops