code: def set_weight_bias(
      self, weight: torch.Tensor, bias: Optional[torch.Tensor]
  ) -> None:
      if self.dtype == torch.qint8:
          self._packed_params = torch.ops.quantized.linear_prepack(
              weight, bias
          )
      elif self.dtype == torch.float16:
          self._packed_params = torch.ops.quantized.linear_prepack_fp16(
              weight, bias
          )
      else:
          raise RuntimeError(
              "Unsupported dtype on dynamic quantized linear!"
          )
  
imports: from collections.abc import Iterable
  import torch
  import torch.nn
  import torch.ao.nn.intrinsic
  import torch.ao.nn.intrinsic.qat
  from torch.nn.utils.fusion import fuse_linear_bn_weights
  from torch.nn.utils.parametrize import type_before_parametrizations
  from typing import Optional
  from .utils import _quantize_weight, _hide_packed_params_repr, WeightedQuantizedModule
label_desc: Use image node_id 2 for calling the LinearPackedParams obj's underlying member method code with example usage: obj.set_weight_bias(weight, bias) without return types