class_docstr: A linear module attached with FakeQuantize modules for weight,
  used for quantization aware training.
  
  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 `torch.nn.Linear`, with FakeQuantize modules initialized to
  default.
  
  Attributes:
      weight: fake quant module for weight
class_docstr_tok: ["A","linear","module","attached","with","FakeQuantize","modules","for","weight",",","used","for","quantization","aware","training",".","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","`","torch.nn.Linear","`",",","with","FakeQuantize","modules","initialized","to","default",".","Attributes",":","weight",":","fake","quant","module","for","weight"]
code: def __init__(
      self,
      in_features,
      out_features,
      bias=True,
      qconfig=None,
      device=None,
      dtype=None,
  ) -> None:
      factory_kwargs = {"device": device, "dtype": dtype}
      super().__init__(
          in_features, out_features, bias, **factory_kwargs
      )
      assert qconfig, "qconfig must be provided for QAT module"
      self.qconfig = qconfig
      self.weight_fake_quant = qconfig.weight(
          factory_kwargs=factory_kwargs
      )
  
file_path: pytorch/torch/ao/nn/qat/modules/linear.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.ao.nn.intrinsic import LinearReLU
  from torch.nn.utils.parametrize import is_parametrized, type_before_parametrizations, transfer_parametrizations_and_params
label_desc: Use image node_id 1 to create a new Linear object from inherited base classes: nn with example: obj = Linear(in_features, out_features, bias, qconfig, device, dtype)