code: def add_n(
      inputs: typing.List[ragged_tensor.RaggedOrDense], name=None
  ):
      """RaggedTensor implementation for tf.math.add_n."""
      if len(inputs) < 0:
          raise ValueError("tf.add_n: expected at least one input.")
      with ops.name_scope(name, "RaggedAddN", inputs):
          return ragged_functional_ops.map_flat_values(
              math_ops.add_n, inputs
          )
  
imports: import functools
  import typing
  import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import gen_ragged_math_ops
  from tensorflow.python.ops import map_fn
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops.ragged import ragged_functional_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.ops.ragged import segment_id_ops
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 28 for calling a global function with example usage: add_n(inputs, name) and returns: ragged_functional_ops