class_docstr: Normalizes a tensor of UTF-8 strings.
class_docstr_tok: ["Normalizes","a","tensor","of","UTF-8","strings","."]
docstr: Initializes the FastBertNormalizer.
  
  Two ways to initialize:
    * use a precompiled `model_buffer`.
    * use `lower_case_nfd_strip_accents`.
  
  Args:
    lower_case_nfd_strip_accents: (optional). - If true, it first lowercases
      the text, applies NFD normalization, strips accents characters, and then
      replaces control characters with whitespaces. - If false, it only
      replaces control characters with whitespaces.
    model_buffer: (optional) bytes object (or a uint8 tf.Tenosr) that contains
      the fast bert normalizer model in flatbuffer format (see
      fast_bert_normalizer_model.fbs). If not `None`, all other arguments are
      ignored.
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code: def __init__(
      self, lower_case_nfd_strip_accents=False, model_buffer=None
  ):
      """Initializes the FastBertNormalizer.
  
      Two ways to initialize:
        * use a precompiled `model_buffer`.
        * use `lower_case_nfd_strip_accents`.
  
      Args:
        lower_case_nfd_strip_accents: (optional). - If true, it first lowercases
          the text, applies NFD normalization, strips accents characters, and then
          replaces control characters with whitespaces. - If false, it only
          replaces control characters with whitespaces.
        model_buffer: (optional) bytes object (or a uint8 tf.Tenosr) that contains
          the fast bert normalizer model in flatbuffer format (see
          fast_bert_normalizer_model.fbs). If not `None`, all other arguments are
          ignored.
      """
      super(FastBertNormalizer, self).__init__()
      _tf_text_fast_bert_normalizer_op_create_counter.get_cell().increase_by(
          1
      )
  
      if model_buffer is None:
          model_buffer = pywrap_fast_bert_normalizer_model_builder.build_fast_bert_normalizer_model(
              lower_case_nfd_strip_accents
          )
      # Use uint8 tensor as a buffer for the model to avoid any possible changes,
      # for example truncation by '\0'.
      if isinstance(model_buffer, tensor.Tensor):
          self._model = model_buffer
      else:
          self._model = constant_op.constant(
              list(model_buffer), dtype=dtypes.uint8
          )
  
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 constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.module import module
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensor
  from tensorflow_text.core.pybinds import pywrap_fast_bert_normalizer_model_builder
  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 FastBertNormalizer object from inherited base classes: module with example: obj = FastBertNormalizer(lower_case_nfd_strip_accents, model_buffer)