code: def load(cls, path: Union[Path, str], **kwargs):
      path = Path(path)
      tflite_file_path = str(
          path / TENSORFLOW_BACKEND_FILENAMES["tflite_model"]
      )
  
      with open(tflite_file_path, "rb") as f:
          tflite_file = f.read()
  
      metadata = LearnerMetadata.read(path)
      network_parameters = ModelParams(**metadata.network_parameters)
      input_tfms = metadata.input_tfms
      device = Device.from_str(metadata.device)
      return cls(
          tflite_file=tflite_file,
          network_parameters=network_parameters,
          input_tfms=input_tfms,
          device=device,
      )
  
imports: import pickle
  from pathlib import Path
  from typing import Tuple, Union, Dict, Type
  from nebullvm.config import TENSORFLOW_BACKEND_FILENAMES
  from nebullvm.core.models import DeviceType, Device, ModelParams
  from nebullvm.operations.inference_learners.base import TensorflowBaseInferenceLearner, LearnerMetadata
  from nebullvm.optional_modules.tensorflow import tensorflow
label_desc: Use image node_id 6 for calling the TFLiteBackendInferenceLearner obj's underlying member method code with example usage: obj.load(cls, path) and returns: cls