code: def _verify_split(input_dim, out_nums):
      dtype = "float32"
  
      a_np = np.random.uniform(-100.0, 100.0, size=input_dim).astype(
          dtype
      )
      ref_val = np.split(a_np, out_nums, axis=-3)
  
      inputs = [("input", datatypes.Array(*input_dim))]
  
      output_names = []
      outputs = []
      output_shapes = []
      for i, out in enumerate(ref_val):
          output_name = "output" + str(i)
          output_names = output_names + [output_name]
          outputs = outputs + [
              (output_name, datatypes.Array(*out.shape))
          ]
          output_shapes = output_shapes + [out.shape]
  
      builder = NeuralNetworkBuilder(inputs, outputs)
      builder.add_split(
          name="split", input_name="input", output_names=output_names
      )
  
      model = cm.models.MLModel(builder.spec)
      for target, dev in tvm.testing.enabled_targets():
          out = run_tvm_graph(
              model,
              target,
              dev,
              [a_np],
              ["input"],
              output_shapes,
              [dtype] * len(output_shapes),
          )
          tvm.testing.assert_allclose(out, ref_val, rtol=1e-5)
  
imports: from os import path
  from enum import Enum
  import tempfile
  import numpy
  import model_zoo
  import coremltools
  from coremltools.models.neural_network import NeuralNetworkBuilder
  from coremltools.models import datatypes
  from tensorflow import keras
  import tvm
  import tvm.topi.testing
  import tvm.testing
  from tvm.contrib import graph_executor
  from tvm.topi.testing import conv2d_nchw_python
  from tvm import relay
label_desc: Use image node_id 36 for calling a global function with example usage: _verify_split(input_dim, out_nums) without return types