class_docstr: Tokenizer that uses a Hub module.
  
  This class is just a wrapper around an internal HubModuleSplitter.  It offers
  the same functionality, but with 'token'-based method names: e.g., one can use
  tokenize() instead of the more general and less informatively named split().
  
  Example:
  
  >>> HUB_MODULE = "https://tfhub.dev/google/zh_segmentation/1"
  >>> segmenter = HubModuleTokenizer(HUB_MODULE)
  >>> segmenter.tokenize(["新华社北京"])
  <tf.RaggedTensor [[b'\xe6\x96\xb0\xe5\x8d\x8e\xe7\xa4\xbe',
                     b'\xe5\x8c\x97\xe4\xba\xac']]>
  
  You can also use this tokenizer to return the split strings and their offsets:
  
  >>> HUB_MODULE = "https://tfhub.dev/google/zh_segmentation/1"
  >>> segmenter = HubModuleTokenizer(HUB_MODULE)
  >>> pieces, starts, ends = segmenter.tokenize_with_offsets(["新华社北京"])
  >>> print("pieces: %s starts: %s ends: %s" % (pieces, starts, ends))
  pieces: <tf.RaggedTensor [[b'\xe6\x96\xb0\xe5\x8d\x8e\xe7\xa4\xbe',
                             b'\xe5\x8c\x97\xe4\xba\xac']]>
  starts: <tf.RaggedTensor [[0, 9]]>
  ends: <tf.RaggedTensor [[9, 15]]>
docstr: Tokenizes a tensor of UTF-8 strings into words.
  
  Args:
    input_strs: An N-dimensional `Tensor` or `RaggedTensor` of UTF-8 strings.
  
  Returns:
    A `RaggedTensor` of segmented text. The returned shape is the shape of the
    input tensor with an added ragged dimension for tokens of each string.
docstr_tok: ["Tokenizes","a","tensor","of","UTF-8","strings","into","words",".","Args",":","input_strs",":","An","N-dimensional","`","Tensor","`","or","`","RaggedTensor","`","of","UTF-8","strings",".","Returns",":","A","`","RaggedTensor","`","of","segmented","text",".","The","returned","shape","is","the","shape","of","the","input","tensor","with","an","added","ragged","dimension","for","tokens","of","each","string","."]
code: def tokenize(self, input_strs):
      """Tokenizes a tensor of UTF-8 strings into words.
  
      Args:
        input_strs: An N-dimensional `Tensor` or `RaggedTensor` of UTF-8 strings.
  
      Returns:
        A `RaggedTensor` of segmented text. The returned shape is the shape of the
        input tensor with an added ragged dimension for tokens of each string.
      """
      return self._splitter.split(input_strs)
  
imports: from tensorflow.python.eager import monitoring
  from tensorflow_text.python.ops import hub_module_splitter
  from tensorflow_text.python.ops.tokenization import TokenizerWithOffsets
label_desc: Use image node_id 3 for calling the HubModuleTokenizer obj's underlying member method code with example usage: obj.tokenize(input_strs) and returns: self