code: def testRandomizeWeights(self):
      # 1. SETUP
      # Define the initial model
      initial_model = test_utils.build_mock_model()
      final_model = copy.deepcopy(initial_model)
  
      # 2. INVOKE
      # Invoke the randomize_weights function
      flatbuffer_utils.randomize_weights(final_model)
  
      # 3. VALIDATE
      # Validate that the initial and final models are the same, except that
      # the weights in the model buffer have been modified (i.e, randomized)
      # Validate the description
      self.assertEqual(
          initial_model.description, final_model.description
      )
      # Validate the main subgraph's name, inputs, outputs, operators and tensors
      initial_subgraph = initial_model.subgraphs[0]
      final_subgraph = final_model.subgraphs[0]
      self.assertEqual(initial_subgraph.name, final_subgraph.name)
      for i in range(len(initial_subgraph.inputs)):
          self.assertEqual(
              initial_subgraph.inputs[i], final_subgraph.inputs[i]
          )
      for i in range(len(initial_subgraph.outputs)):
          self.assertEqual(
              initial_subgraph.outputs[i], final_subgraph.outputs[i]
          )
      for i in range(len(initial_subgraph.operators)):
          self.assertEqual(
              initial_subgraph.operators[i].opcodeIndex,
              final_subgraph.operators[i].opcodeIndex,
          )
      initial_tensors = initial_subgraph.tensors
      final_tensors = final_subgraph.tensors
      for i in range(len(initial_tensors)):
          self.assertEqual(
              initial_tensors[i].name, final_tensors[i].name
          )
          self.assertEqual(
              initial_tensors[i].type, final_tensors[i].type
          )
          self.assertEqual(
              initial_tensors[i].buffer, final_tensors[i].buffer
          )
          for j in range(len(initial_tensors[i].shape)):
              self.assertEqual(
                  initial_tensors[i].shape[j], final_tensors[i].shape[j]
              )
      # Validate the first valid buffer (index 0 is always None)
      initial_buffer = initial_model.buffers[1].data
      final_buffer = final_model.buffers[1].data
      for j in range(initial_buffer.size):
          self.assertNotEqual(
              initial_buffer.data[j], final_buffer.data[j]
          )
  
imports: import copy
  import os
  import subprocess
  import sys
  from tensorflow.lite.tools import flatbuffer_utils
  from tensorflow.lite.tools import test_utils
  from tensorflow.python.framework import test_util
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the RandomizeWeightsTest obj's underlying member method code with example usage: obj.testRandomizeWeights() without return types