code: def _construct_constexpr_affine_op(
      quantized_weights, zero_point, scale, axis=None, name=None
  ):
      """Constructs the constexpr op to represent the dequantized weight from PyTorch's data."""
      if axis is None:
          # It's per-tensor quantization, just use a dummy value for axis.
          axis = _np.int32(0)
      kwargs = {
          "quantized_data": quantized_weights,
          "zero_point": zero_point,
          "scale": scale,
          "axis": axis,
      }
      if name is not None:
          kwargs["name"] = name
      return mb.constexpr_affine_dequantize(**kwargs)
  
imports: import numpy
  import torch
  from coremltools import _logger
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Var, types
  from .ops import NUM_TO_TORCH_DTYPE, _create_linear_layer, _get_inputs, promote_input_dtypes
  from .torch_op_registry import register_torch_op
label_desc: Use image node_id 5 for calling a global function with example usage: _construct_constexpr_affine_op(quantized_weights, zero_point, scale, axis, name) and returns: mb