code: def verify_multiply_layer_params(input_dim, alpha):
      """Verify multiply layer params"""
      dtype = "float32"
  
      a_np1 = np.random.uniform(size=input_dim).astype(dtype)
      a_np2 = np.random.uniform(size=input_dim).astype(dtype)
  
      b_np = np.multiply(a_np1, a_np2) * alpha
      inputs = [
          ("input1", datatypes.Array(*input_dim)),
          ("input2", datatypes.Array(*input_dim)),
      ]
      output = [("output", datatypes.Array(*b_np.shape))]
      builder = NeuralNetworkBuilder(inputs, output)
      builder.add_elementwise(
          name="Mul",
          alpha=alpha,
          input_names=["input1", "input2"],
          output_name="output",
          mode="MULTIPLY",
      )
      model = cm.models.MLModel(builder.spec)
      for target, dev in tvm.testing.enabled_targets():
          out = run_tvm_graph(
              model,
              target,
              dev,
              [a_np1, a_np2],
              ["input1", "input2"],
              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 8 for calling a global function with example usage: verify_multiply_layer_params(input_dim, alpha) without return types