class_docstr: This transform works for each ``const`` op if:
  
  - ``_is_deprecated=True`` and the ``op_selector`` returns ``True``.
  - ``_is_deprecated=False`` and the ``const`` value size ``> weight_threshold``.
  
  The transform performs the following:
  
  - A linear look-up table (LUT) with 2\ :sup:`nbits` entries is created with values represented by indexing into this LUT.
  - If ``fake_compression=False``, compressed value is encoded using the ``constexpr_lut_to_dense`` op.
  - If ``fake_compression=True``,  compressed value is decompressed and then encoded using the ``const`` op.
  - Old ``const`` op is replaced by a newly created operation.
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code: def transform_op(self, op: Operation):
      op_config = self.config._get_const_op_config(op)
      if op_config is None:
          return
      if not self.need_compress_const(
          op, self.config._is_deprecated, op_config.weight_threshold
      ):
          return
  
      if op_config.mode == "UNIQUE":
          try:
              palettize_weights._get_nbits_for_unique_mode(
                  op.outputs[0].val, self._SUPPORTED_NBITS
              )
          except ValueError as e:
              logger.warning(
                  f"Skip op {op.name} for palettization, because {e}"
              )
              return
  
      lut_params = self.compress(
          op.outputs[0].val,
          op_config.mode,
          op_config.nbits,
          op_config.lut_function,
      )
  
      if not self.fake_compression:
          new_var = mb.constexpr_lut_to_dense(
              indices=lut_params.indices,
              lut=lut_params.lut,
              shape=np.uint32(lut_params.shape),
              before_op=op,
              name=op.name + "_palettized",
          )
      else:
          decompressed_val = self.decompress(lut_params)
          new_var = mb.const(
              val=decompressed_val,
              before_op=op,
              name=op.name + "_fake_palettized",
          )
  
      op.enclosing_block.replace_uses_of_var_after_op(
          anchor_op=op,
          old_var=op.outputs[0],
          new_var=new_var,
          no_check_var_types=True,
      )
  
      op.enclosing_block.remove_ops([op])
  
imports: from typing import Callable, Optional, Tuple
  import numpy
  from tqdm import tqdm
  from coremltools import _logger
  from coremltools.converters.mil._deployment_compatibility import AvailableTarget
  from coremltools.converters.mil.backend.mil.load import should_use_weight_file
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Operation, Program, types
  from coremltools.converters.mil.mil.block import is_current_opset_version_compatible_with
  from coremltools.converters.mil.mil.ops.defs._utils import pack_elements_into_bits
  from coremltools.converters.mil.mil.ops.defs.iOS16 import constexpr_affine_dequantize, constexpr_lut_to_dense, constexpr_sparse_to_dense
  from coremltools.converters.mil.mil.passes.defs.quantization import AbstractQuantizationPass
  from coremltools.converters.mil.mil.passes.helper import block_context_manager
  from coremltools.converters.mil.mil.passes.pass_registry import register_pass
  from coremltools.converters.mil.mil.types.type_mapping import nptype_from_builtin
  from coremltools.models.neural_network.quantization_utils import _get_kmeans_lookup_table_and_weight
  from coremltools.optimize.coreml._config import OpLinearQuantizerConfig, OpMagnitudePrunerConfig, OpPalettizerConfig, OpThresholdPrunerConfig, OptimizationConfig
label_desc: Use image node_id 6 for calling the palettize_weights obj's underlying member method code with example usage: obj.transform_op(op) without return types