code: def apply_function_with_checkpoint(
      fn, inputs, checkpoint, include=None, exclude=None
  ):
      """Applies a tensor-in-tensor-out function with variables to some `Tensor`s.
  
      Variable values are loaded from the given checkpoint path. Note that the
      input_tensor_func, together with the checkpoint, will be converted to an
      all-constants graph, so ops requiring graph collections, such as table lookup
      (which requires a table init op being added to TABLE_INITIALIZERS collection),
      are not supported.
  
      Note: This API can only be used when TF2 is disabled or
      `tft_beam.Context.force_tf_compat_v1=True`.
  
      Args:
        fn: A tensor-in-tensor-out function that may contain variables.
        inputs: A list of `Tensor`s to apply `fn` to.
        checkpoint: The checkpoint path to load variables from.
        include: An optional list/tuple of scope strings for filtering which
            variables from the VARIABLES collection to include. If None, all
            variables will be included.
        exclude: An optional list/tuple of scope strings for filtering which
            variables from the VARIABLES collection to exclude. If None, no
            variables will be excluded.
  
      Returns:
        A `Tensor` or list of `Tensor`s representing the application of `fn`.
  
      Raises:
        ValueError: if the input tensor-in-tensor-out function adds to
            TABLE_INITIALIZERS collections.
      """
      loaded_graph = tf.compat.v1.Graph()
      with loaded_graph.as_default():
          input_placeholders = [
              tf.compat.v1.placeholder(
                  dtype=tensor.dtype,
                  shape=tensor.shape,
                  name=tensor.op.name,
              )
              for tensor in inputs
          ]
          output = fn(*input_placeholders)
          if isinstance(output, tf.Tensor):
              output_tensors = [output]
              output_single_tensor = True
          else:
              output_tensors = output
              output_single_tensor = False
  
          # TODO(qimingj/kestert): Copy table initializers to the composed graph.
          if tf.compat.v1.get_collection(
              tf.compat.v1.GraphKeys.TABLE_INITIALIZERS
          ):
              raise ValueError(
                  "Models with table init ops are not supported."
              )
  
          vars_to_restore = _get_variables_to_restore(
              include=include, exclude=exclude
          )
          saver = tf.compat.v1.train.Saver(vars_to_restore)
          with tf.compat.v1.Session() as sess:
              saver.restore(sess, checkpoint)
              output_graph_def = tf.compat.v1.graph_util.convert_variables_to_constants(
                  sess,
                  loaded_graph.as_graph_def(),
                  [tensor.op.name for tensor in output_tensors],
              )
  
      input_map = {tensor.name: tensor for tensor in inputs}
      output_tensors = tf.import_graph_def(
          output_graph_def,
          input_map=input_map,
          return_elements=[tensor.name for tensor in output_tensors],
      )
  
      if output_single_tensor:
          assert len(output_tensors) == 1
          return output_tensors[0]
      else:
          return output_tensors
  
imports: import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: apply_function_with_checkpoint(fn, inputs, checkpoint, include, exclude) and returns: output_tensors, output_tensors