class_docstr: Indicates a position within a `_CheckpointRestoreCoordinator`.
class_docstr_tok: ["Indicates","a","position","within","a","`","_CheckpointRestoreCoordinator","`","."]
docstr: The shape of the VARIABLE_VALUE tensor.
  
  Returns:
    If found a TensorShape object, otherwise None.
docstr_tok: ["The","shape","of","the","VARIABLE_VALUE","tensor",".","Returns",":","If","found","a","TensorShape","object",",","otherwise","None","."]
code: def value_shape(self):
      """The shape of the VARIABLE_VALUE tensor.
  
      Returns:
        If found a TensorShape object, otherwise None.
      """
      for serialized_tensor in self.object_proto.attributes:
          if serialized_tensor.name == constants.VARIABLE_VALUE_KEY:
              return self._checkpoint.shape_map[
                  serialized_tensor.checkpoint_key
              ]
      return None
  
imports: import collections
  from tensorflow.python.checkpoint import checkpoint_view
  from tensorflow.python.checkpoint import functional_saver
  from tensorflow.python.checkpoint import save_util_v1
  from tensorflow.python.checkpoint import saveable_compat
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_io_ops
  from tensorflow.python.ops import io_ops
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.saved_model import registration
  from tensorflow.python.trackable import base
  from tensorflow.python.trackable import constants
  from tensorflow.python.trackable import python_state
  from tensorflow.python.trackable import trackable_utils
  from tensorflow.python.training.saving import saveable_object_util
  from tensorflow.python.util import object_identity
label_desc: Use image node_id 16 for calling the CheckpointPosition obj's underlying member method code with example usage: obj.value_shape() and returns: None, self