code: def graph_context_for_symbolic_tensors(*args, **kwargs):
      """Returns graph context manager if any of the inputs is a symbolic tensor."""
      if any(
          is_symbolic_tensor(v)
          for v in list(args) + list(kwargs.values())
      ):
          with K.get_graph().as_default():
              yield
      else:
          yield
  
imports: import collections
  import copy
  import numpy
  from tensorflow.python.data.experimental.ops import cardinality
  from tensorflow.python.distribute.coordinator import cluster_coordinator
  from tensorflow.python.eager import context
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.framework import type_spec
  from tensorflow.python.keras import backend
  from tensorflow.python.keras.engine import keras_tensor
  from tensorflow.python.keras.utils import object_identity
  from tensorflow.python.keras.utils import tf_contextlib
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.ops.ragged import ragged_tensor_value
  from tensorflow.python.util import nest
label_desc: Use image node_id 18 for calling a global function with example usage: graph_context_for_symbolic_tensors() without return types