code: def model_shapes(checkpoint_path, variables_to_get=None):
      """Returns the shape of each tensor in the model at checkpoint_path.
  
      Args:
        checkpoint_path: string. The path to a tensorflow checkpoint containing a
          model whose tensor shapes we want to get.
        variables_to_get: options, list of strings. If provided, only returns the
          shapes of tensors in variables whose name is present in this list. If
          None, the default, gets all of the tensors. A KeyError will be raised if
          any variable name in variables_to_get isn't present in the checkpointed
          model.
  
      Returns:
        A dictionary mapping variable names [string] to tensor shapes [tuple].
      """
      reader = tf.compat.v1.train.NewCheckpointReader(checkpoint_path)
      var_to_shape_map = reader.get_variable_to_shape_map()
      keys = (
          variables_to_get
          if variables_to_get
          else var_to_shape_map.keys()
      )
      return {key: tuple(var_to_shape_map[key]) for key in keys}
  
imports: import json
  from typing import Optional
  from absl import logging
  import numpy
  import tensorflow
  from deepvariant.protos import deepvariant_pb2
  from third_party.nucleus.io import sharded_file_utils
  from third_party.nucleus.io import tfrecord
  from third_party.nucleus.protos import variants_pb2
  from tensorflow.core.example import example_pb2
label_desc: Use image node_id 18 for calling a global function with example usage: model_shapes(checkpoint_path, variables_to_get) and returns: unknown