class_docstr: Tokenizes a tensor of UTF-8 strings on whitespaces.
class_docstr_tok: ["Tokenizes","a","tensor","of","UTF-8","strings","on","whitespaces","."]
docstr: Tokenizes a tensor of codepoints with rank of 1.
  
  Args:
    input_tensor: Single-dimension Tensor of strings to tokenize.
  
  Returns:
    Tuple of tokenized codepoints with offsets relative to the codepoints have
    a shape of [num_strings, (num_tokens or num_offsets)].
docstr_tok: ["Tokenizes","a","tensor","of","codepoints","with","rank","of","1",".","Args",":","input_tensor",":","Single-dimension","Tensor","of","strings","to","tokenize",".","Returns",":","Tuple","of","tokenized","codepoints","with","offsets","relative","to","the","codepoints","have","a","shape","of","[","num_strings",",","(","num_tokens","or","num_offsets",")","]","."]
code: def _whitespace_tokenize_with_offsets(self, input_tensor):
      """Tokenizes a tensor of codepoints with rank of 1.
  
      Args:
        input_tensor: Single-dimension Tensor of strings to tokenize.
  
      Returns:
        Tuple of tokenized codepoints with offsets relative to the codepoints have
        a shape of [num_strings, (num_tokens or num_offsets)].
      """
      (
          values,
          row_splits,
          start_offsets,
          end_offsets,
      ) = gen_whitespace_tokenizer_v2.tf_text_whitespace_tokenize_with_offsets_v2(
          input_values=input_tensor, input_config=self._config
      )
      values = RaggedTensor.from_nested_row_splits(
          flat_values=values, nested_row_splits=[row_splits]
      )
      start_offsets = RaggedTensor.from_nested_row_splits(
          flat_values=start_offsets, nested_row_splits=[row_splits]
      )
      end_offsets = RaggedTensor.from_nested_row_splits(
          flat_values=end_offsets, nested_row_splits=[row_splits]
      )
      return (values, start_offsets, end_offsets)
  
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 4 for calling the WhitespaceTokenizer obj's underlying member method code with example usage: obj._whitespace_tokenize_with_offsets(input_tensor) and returns: values,  start_offsets,  end_offsets