code: def are_compatible(spec1, spec2):
      """Indicates whether two type specifications are compatible.
  
      Two type specifications are compatible if they have the same nested structure
      and the their individual components are pair-wise compatible.
  
      Args:
        spec1: A `tf.TypeSpec` object to compare.
        spec2: A `tf.TypeSpec` object to compare.
  
      Returns:
        `True` if the two type specifications are compatible and `False` otherwise.
      """
  
      try:
          nest.assert_same_structure(spec1, spec2)
      except TypeError:
          return False
      except ValueError:
          return False
  
      for s1, s2 in zip(nest.flatten(spec1), nest.flatten(spec2)):
          if not s1.is_compatible_with(s2) or not s2.is_compatible_with(
              s1
          ):
              return False
      return True
  
imports: import collections
  import functools
  import itertools
  import wrapt
  from tensorflow.python.data.util import nest
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.framework import none_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 type_spec
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.types import internal
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.compat import collections_abc
  from tensorflow.python.util.nest_util import CustomNestProtocol
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 16 for calling a global function with example usage: are_compatible(spec1, spec2) and returns: True, False, False, False