code: def util_batch_unbatch_ragged(
      model: Model[Ragged, Array2d], in_data: Ragged, out_data: Array2d
  ):
      with data_validation(True):
          model.initialize(in_data, out_data)
          Y_batched = model.predict(in_data)
          Y_not_batched = [
              model.predict(in_data[i])[0] for i in range(len(in_data))
          ]
          assert_almost_equal(Y_batched, Y_not_batched, decimal=4)
  
imports: from typing import List, Optional
  import numpy
  import pytest
  import srsly
  from numpy.testing import assert_almost_equal
  from thinc.api import Dropout, Model, NumpyOps, registry, with_padded
  from thinc.backends import NumpyOps
  from thinc.compat import has_torch
  from thinc.types import Array2d, Floats2d, FloatsXd, Padded, Ragged, Shape
  from thinc.util import data_validation, get_width
label_desc: Use image node_id 8 for calling a global function with example usage: util_batch_unbatch_ragged(model, in_data, out_data) without return types