class_docstr: A reference quantized linear module that fits into the FX
  Graph Mode Quantization workflow
  activation will be floating point Tensor, we will store floating
  point weight as well in the module, but in forward we'll quantize
  and dequantize the weight before running the floating point functional
  linear operator.
class_docstr_tok: ["A","reference","quantized","linear","module","that","fits","into","the","FX","Graph","Mode","Quantization","workflow","activation","will","be","floating","point","Tensor",",","we","will","store","floating","point","weight","as","well","in","the","module",",","but","in","forward","we","'ll","quantize","and","dequantize","the","weight","before","running","the","floating","point","functional","linear","operator","."]
code: def from_float(cls, float_linear, weight_qparams):
      qref_linear = Linear(
          float_linear.in_features,
          float_linear.out_features,
          float_linear.bias is not None,
          device=float_linear.weight.device,
          dtype=float_linear.weight.dtype,
          weight_qparams=weight_qparams,
      )
      qref_linear.weight = torch.nn.Parameter(
          float_linear.weight.detach()
      )
      if float_linear.bias is not None:
          qref_linear.bias = torch.nn.Parameter(
              float_linear.bias.detach()
          )
      return qref_linear
  
file_path: pytorch/torch/ao/nn/quantized/reference/modules/linear.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from typing import Optional, Dict, Any
  from .utils import ReferenceQuantizedModule
label_desc: Use image node_id 4 for calling the Linear obj's underlying member method code with example usage: obj.from_float(cls, float_linear, weight_qparams) and returns: qref_linear