code: def load(cls, path: Union[Path, str], **kwargs):
      path = Path(path)
      metadata = LearnerMetadata.read(path)
      network_parameters = ModelParams(**metadata.network_parameters)
      input_tfms = metadata.input_tfms
      model = tf.keras.models.load_model(
          path / TENSORFLOW_BACKEND_FILENAMES["tf_model"]
      )
      device = Device.from_str(metadata.device)
      return cls(
          tf_model=model,
          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 5 for calling the TensorflowBackendInferenceLearner obj's underlying member method code with example usage: obj.load(cls, path) and returns: cls