class_docstr: Specification for a BERT model.
  
  See https://arxiv.org/abs/1810.04805 (BERT: Pre-training of Deep Bidirectional
  Transformers for Language Understanding) for more details.
  
    Attributes:
      files: Either a TFHub url string which can be passed directly to
        hub.KerasLayer or a DownloadedFiles object of the model files.
      is_tf2: If True, the checkpoint is TF2 format. Else use TF1 format.
      hparams: Hyperparameters used for training.
      model_options: Configurable options for a BERT model.
      do_lower_case: boolean, whether to lower case the input text. Should be
        True / False for uncased / cased models respectively, where the models
        are specified by `downloaded_files`.
      tflite_input_name: Dict, input names for the TFLite model.
      name: The name of the object.
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code: def get_path(self) -> str:
      if isinstance(self.files, file_util.DownloadedFiles):
          return self.files.get_path()
      elif isinstance(self.files, str):
          return self.files
      else:
          raise ValueError(
              f"files has unsupported type: {type(self.files)}"
          )
  
imports: import dataclasses
  from typing import Dict, Union
  from mediapipe.model_maker.python.core import hyperparameters
  from mediapipe.model_maker.python.core.utils import file_util
  from mediapipe.model_maker.python.text.core import bert_model_options
label_desc: Use image node_id 1 for calling the BertModelSpec obj's underlying member method code with example usage: obj.get_path() and returns: self, self