code: def _weight_bias(self):
      if self.dtype == torch.qint8:
          return torch.ops.quantized.linear_unpack(self._packed_params)
      elif self.dtype == torch.float16:
          return torch.ops.quantized.linear_unpack_fp16(
              self._packed_params
          )
      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 3 for calling the LinearPackedParams obj's underlying member method code with example usage: obj._weight_bias() and returns: torch, torch