class_docstr: TFLite metrics helper for prod (borg) environment.
  
  Attributes:
    model_hash: A string containing the hash of the model binary.
    model_path: A string containing the path of the model for debugging
      purposes.
code: def __init__(
      self,
      model_hash: Optional[Text] = None,
      model_path: Optional[Text] = None,
  ) -> None:
      del self  # Temporarily removing self until parameter logic is implemented.
      if model_hash and not model_path or not model_hash and model_path:
          raise ValueError(
              "Both model metadata(model_hash, model_path) should be "
              "given at the same time."
          )
      if model_hash:
          # TODO(b/180400857): Create stub once the service is implemented.
          pass
  
imports: from typing import Optional, Text
  import uuid
  from tensorflow.lite.python.metrics import converter_error_data_pb2
  from tensorflow.lite.python.metrics import metrics_interface
  from tensorflow.lite.python.metrics.wrapper import metrics_wrapper
  from tensorflow.python.eager import monitoring
label_desc: Use image node_id 1 to create a new TFLiteMetrics object from inherited base classes: metrics_interface with example: obj = TFLiteMetrics(model_hash, model_path)