class_docstr: Tokenizes a tensor of UTF-8 strings on whitespaces.
class_docstr_tok: ["Tokenizes","a","tensor","of","UTF-8","strings","on","whitespaces","."]
docstr: Initializes the WhitespaceTokenizer.
      
docstr_tok: ["Initializes","the","WhitespaceTokenizer","."]
code: def __init__(self):
      """Initializes the WhitespaceTokenizer."""
      super(WhitespaceTokenizer, self).__init__()
      self._config = (
          pywrap_whitespace_tokenizer_config_builder.build_whitespace_tokenizer_config()
      )
      _tf_text_whitespace_tokenizer_op_create_counter.get_cell().increase_by(
          1
      )
  
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 ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops.ragged import ragged_conversion_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensor
  from tensorflow_text.core.pybinds import pywrap_whitespace_tokenizer_config_builder
  from tensorflow_text.python.ops.tokenization import TokenizerWithOffsets
  from tensorflow.python.framework import load_library
  from tensorflow.python.platform import resource_loader
  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 WhitespaceTokenizer object from inherited base classes: TokenizerWithOffsets with example: obj = WhitespaceTokenizer()