class_docstr: A quantized linear module with quantized tensor as inputs and outputs.
  We adopt the same interface as `torch.nn.Linear`, please see
  https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.
  
  Similar to :class:`~torch.nn.Linear`, attributes will be randomly
  initialized at module creation time and will be overwritten later
  
  Attributes:
      weight (Tensor): the non-learnable quantized weights of the module of
                       shape :math:`(\text{out\_features}, \text{in\_features})`.
      bias (Tensor): the non-learnable bias of the module of shape :math:`(\text{out\_features})`.
              If :attr:`bias` is ``True``, the values are initialized to zero.
      scale: `scale` parameter of output Quantized Tensor, type: double
      zero_point: `zero_point` parameter for output Quantized Tensor, type: long
  
  Examples::
  
      >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
      >>> m = nn.quantized.Linear(20, 30)
      >>> input = torch.randn(128, 20)
      >>> # xdoctest: +SKIP
      >>> input = torch.quantize_per_tensor(input, 1.0, 0, torch.quint8)
      >>> output = m(input)
      >>> print(output.size())
      torch.Size([128, 30])
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code: def _save_to_state_dict(self, destination, prefix, keep_vars):
      super()._save_to_state_dict(destination, prefix, keep_vars)
      destination[prefix + "scale"] = torch.tensor(self.scale)
      destination[prefix + "zero_point"] = torch.tensor(self.zero_point)
  
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 6 for calling the Linear obj's underlying member method code with example usage: obj._save_to_state_dict(destination, prefix, keep_vars) without return types