class_docstr: Indicates a position within a `_CheckpointRestoreCoordinator`.
class_docstr_tok: ["Indicates","a","position","within","a","`","_CheckpointRestoreCoordinator","`","."]
docstr: Generates CheckpointPosition for a slot variable.
  
  Args:
    optimizer_object: Optimizer that owns the slot variable.
    variable: Variable associated with the slot variable.
    slot_variable_id: ID of the slot variable.
    slot_name: Name of the slot variable.
  
  Returns:
    If there is a slot variable in the `optimizer_object` that has not been
    bound to the checkpoint, this function returns a tuple of (
      new `CheckpointPosition` for the slot variable,
      the slot variable itself).
docstr_tok: ["Generates","CheckpointPosition","for","a","slot","variable",".","Args",":","optimizer_object",":","Optimizer","that","owns","the","slot","variable",".","variable",":","Variable","associated","with","the","slot","variable",".","slot_variable_id",":","ID","of","the","slot","variable",".","slot_name",":","Name","of","the","slot","variable",".","Returns",":","If","there","is","a","slot","variable","in","the","`","optimizer_object","`","that","has","not","been","bound","to","the","checkpoint",",","this","function","returns","a","tuple","of","(","new","`","CheckpointPosition","`","for","the","slot","variable",",","the","slot","variable","itself",")","."]
code: def create_slot_variable_position(
      self, optimizer_object, variable, slot_variable_id, slot_name
  ):
      """Generates CheckpointPosition for a slot variable.
  
      Args:
        optimizer_object: Optimizer that owns the slot variable.
        variable: Variable associated with the slot variable.
        slot_variable_id: ID of the slot variable.
        slot_name: Name of the slot variable.
  
      Returns:
        If there is a slot variable in the `optimizer_object` that has not been
        bound to the checkpoint, this function returns a tuple of (
          new `CheckpointPosition` for the slot variable,
          the slot variable itself).
      """
      slot_variable_position = CheckpointPosition(
          checkpoint=self.checkpoint, proto_id=slot_variable_id
      )
      # pylint: disable=protected-access
      slot_variable = optimizer_object._create_or_restore_slot_variable(
          slot_variable_position=slot_variable_position,
          variable=variable,
          slot_name=slot_name,
      )
      # pylint: enable=protected-access
      if (
          slot_variable is not None
          and slot_variable_position.bind_object(slot_variable)
      ):
          return slot_variable_position, slot_variable
      else:
          return None, 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 19 for calling the CheckpointPosition obj's underlying member method code with example usage: obj.create_slot_variable_position(optimizer_object, variable, slot_variable_id, slot_name) and returns: slot_variable_position,  slot_variable, None,  None