code: def _verify_lrn(input_dim, size, bias, alpha, beta):
      dtype = "float32"
      axis = 1
      a_np = np.random.uniform(size=input_dim).astype(dtype)
      b_np = tvm.topi.testing.lrn_python(
          a_np, size, axis, bias, alpha, beta
      )
  
      input_data = [("input", datatypes.Array(*input_dim))]
      output = [("output", datatypes.Array(*b_np.shape))]
      builder = NeuralNetworkBuilder(input_data, output)
      builder.add_lrn(
          name="LRN",
          input_name="input",
          output_name="output",
          alpha=alpha,
          beta=beta,
          k=bias,
          local_size=size,
      )
  
      model = cm.models.MLModel(builder.spec)
      for target, dev in tvm.testing.enabled_targets():
          out = run_tvm_graph(
              model, target, dev, a_np, "input", b_np.shape, dtype
          )
          tvm.testing.assert_allclose(out, b_np, 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 16 for calling a global function with example usage: _verify_lrn(input_dim, size, bias, alpha, beta) without return types