class_docstr: This class is testing the examples in https://coremltools.readme.io/v7.0/docs/data-dependent-palettization
code: def test_palettizer(self):
      import torch
      import torch.nn as nn
  
      import coremltools as ct
      from coremltools.optimize.torch.palettization import (
          DKMPalettizer,
          DKMPalettizerConfig,
      )
  
      # Toy example
      x, label = torch.rand(1, 4), torch.rand(1, 4)
      data = [(x, label)]
  
      model = nn.Sequential(
          nn.Linear(4, 500),
          nn.Sigmoid(),
          nn.Linear(500, 4),
          nn.Sigmoid(),
      )
      loss_fn = nn.MSELoss()
      optimizer = torch.optim.SGD(
          model.parameters(), lr=0.01, momentum=0.9
      )
  
      # Prepare model for palettization
      module_config = {
          nn.Linear: {
              "n_bits": 2,
              "weight_threshold": 1000,
              "milestone": 2,
          }
      }
      config = DKMPalettizerConfig.from_dict(
          {"module_type_configs": module_config}
      )
      palettizer = DKMPalettizer(model, config)
  
      prepared_model = palettizer.prepare()
  
      # Fine-tune the model for a few epochs after this.
      for inputs, labels in data:
          output = model(inputs)
          loss = loss_fn(output, labels)
          loss.backward()
          optimizer.step()
          palettizer.step()
  
      # prepare for conversion
      finalized_model = palettizer.finalize()
  
      # trace and convert
      example_input = torch.rand(1, 4)  # shape of input for the model
      traced_model = torch.jit.trace(finalized_model, example_input)
  
      coreml_model = ct.convert(
          traced_model,
          convert_to="mlprogram",
          inputs=[ct.TensorType(shape=example_input.shape)],
          pass_pipeline=ct.PassPipeline.DEFAULT_PALETTIZATION,
          minimum_deployment_target=ct.target.iOS16,
      )
  
      assert coreml_model is not None
      output_file = tempfile.NamedTemporaryFile(
          suffix=".mlpackage"
      ).name
      coreml_model.save(output_file)
  
imports: import tempfile
  from coremltools.converters.mil.testing_utils import get_op_types_in_program
  from coremltools.test.optimize.coreml.test_passes import TestCompressionPasses
  from coremltools.test.optimize.coreml.test_passes import TestConfigurationFromDictFromYaml
label_desc: Use image node_id 1 for calling the TestTrainingTimePalettization obj's underlying member method code with example usage: obj.test_palettizer() without return types