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 compress(val, mode, nbits=None, lut_function=None) -> LutParams:
      def check_lut_parameters_are_valid(val, lut, indices):
          if not isinstance(lut, np.ndarray) or not isinstance(
              indices, np.ndarray
          ):
              raise ValueError(
                  "LUT and indices must be type of numpy array."
              )
  
          if indices.size != val.size:
              msg = "Indices size ({}) mismatched with the original weight({}).".format(
                  indices.size, val.size
              )
              raise ValueError(msg)
  
          if len(indices.shape) != 1 or indices.dtype != np.uint8:
              msg = "Indices must be a numpy vector of type uint8. Found shape {} with type {}".format(
                  indices.shape, indices.dtype
              )
              raise ValueError(msg)
  
          if lut.dtype != val.dtype:
              msg = "Dtype mismatched between LUT ({}) and weight ({})".format(
                  lut.dtype, val.dtype
              )
              raise ValueError(msg)
  
      if not isinstance(val, (np.ndarray, np.generic)):
          raise ValueError(
              f"Only numpy arrays are supported. Got {type(val)}"
          )
  
      lut, indices = palettize_weights._get_lut_and_indices(
          val, mode, nbits, lut_function
      )
  
      check_lut_parameters_are_valid(val, lut, indices)
  
      params = LutParams()
      params.lut = lut
      params.shape = val.shape
      params.indices = pack_elements_into_bits(
          indices, int(np.log2(lut.shape[0]))
      )
      return params
  
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 4 for calling the palettize_weights obj's underlying member method code with example usage: obj.compress(val, mode, nbits, lut_function) and returns: params