class_docstr: A :py:class:`~ray.train.Checkpoint` with TensorFlow-specific functionality.
docstr: Retrieve the model stored in this checkpoint.
  
  Returns:
      The Tensorflow Keras model stored in the checkpoint.
docstr_tok: ["Retrieve","the","model","stored","in","this","checkpoint",".","Returns",":","The","Tensorflow","Keras","model","stored","in","the","checkpoint","."]
code: def get_model(
      self,
  ) -> tf.keras.Model:
      """Retrieve the model stored in this checkpoint.
  
      Returns:
          The Tensorflow Keras model stored in the checkpoint.
      """
      metadata = self.get_metadata()
      if self.MODEL_FILENAME_KEY not in metadata:
          raise ValueError(
              "`TensorflowCheckpoint` cannot retrieve the model if you override the "
              "checkpoint metadata. Please use `Checkpoint.update_metadata` instead."
          )
      model_filename = metadata[self.MODEL_FILENAME_KEY]
      with self.as_directory() as checkpoint_dir:
          model_path = os.path.join(checkpoint_dir, model_filename)
          return keras.models.load_model(model_path)
  
file_path: ray/python/ray/train/tensorflow/tensorflow_checkpoint.py
imports: import os
  import shutil
  import tempfile
  from typing import TYPE_CHECKING, Optional
  import tensorflow
  from tensorflow import keras
  from ray.train._internal.framework_checkpoint import FrameworkCheckpoint
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 4 for calling the TensorflowCheckpoint obj's underlying member method code with example usage: obj.get_model() and returns: keras