code: def validate_synchronization_aggregation_trainable(
      synchronization, aggregation, trainable, name
  ):
      """Given user-provided variable properties, sets defaults and validates."""
      if aggregation is None:
          aggregation = variables.VariableAggregation.NONE
      else:
          if not isinstance(
              aggregation,
              (
                  variables.VariableAggregation,
                  variables.VariableAggregationV2,
              ),
          ):
              try:
                  aggregation = variables.VariableAggregationV2(
                      aggregation
                  )
              except ValueError:
                  raise ValueError(
                      "Invalid variable aggregation mode: {} for variable: {}".format(
                          aggregation, name
                      )
                  )
      if synchronization is None:
          synchronization = variables.VariableSynchronization.AUTO
      else:
          try:
              synchronization = variables.VariableSynchronization(
                  synchronization
              )
          except ValueError:
              raise ValueError(
                  "Invalid variable synchronization mode: {} for variable: {}".format(
                      synchronization, name
                  )
              )
      if trainable is None:
          trainable = (
              synchronization
              != variables.VariableSynchronization.ON_READ
          )
      return synchronization, aggregation, trainable
  
imports: import functools
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.keras.engine import base_layer
  from tensorflow.python.keras.utils import tf_contextlib
  from tensorflow.python.keras.utils import tf_inspect
  from tensorflow.python.module import module
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import tf_decorator
label_desc: Use image node_id 6 for calling a global function with example usage: validate_synchronization_aggregation_trainable(synchronization, aggregation, trainable, name) and returns: synchronization,  aggregation,  trainable