code: def testRandomizeSomeWeights(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, but skip the first buffer
      flatbuffer_utils.randomize_weights(
          final_model, buffers_to_skip=[_SKIPPED_BUFFER_INDEX]
      )
  
      # 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 enumerate(initial_subgraph.inputs):
          self.assertEqual(
              initial_subgraph.inputs[i], final_subgraph.inputs[i]
          )
      for i, _ in enumerate(initial_subgraph.outputs):
          self.assertEqual(
              initial_subgraph.outputs[i], final_subgraph.outputs[i]
          )
      for i, _ in enumerate(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 enumerate(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 that the skipped buffer is unchanged.
      initial_buffer = initial_model.buffers[_SKIPPED_BUFFER_INDEX].data
      final_buffer = final_model.buffers[_SKIPPED_BUFFER_INDEX].data
      for j in range(initial_buffer.size):
          self.assertEqual(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 2 for calling the RandomizeWeightsTest obj's underlying member method code with example usage: obj.testRandomizeSomeWeights() without return types