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:
  
  - The fraction of values with the least absolute value are zeroed out (self.sparsity).
  - If ``fake_compression=False``, the zeroed-out value is encoded using the ``constexpr_sparse_to_dense`` op.
  - If ``fake_compression=True``, the zeroed-out value is encoded using the ``const`` op.
  - Old ``const`` is replaced by a new operation with zeroed-out value.
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code: def _pack_val_to_sparse_param(val):
      flattened_val = val.flatten()
      params = SparseParams()
      params.nonzero_data = flattened_val[np.where(flattened_val != 0)]
      params.mask = np.packbits(
          np.where(flattened_val != 0, 1, 0), bitorder="little"
      )
      params.shape = val.shape
      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 2 for calling the prune_weights obj's underlying member method code with example usage: obj._pack_val_to_sparse_param(val) and returns: params