class_docstr: This is the quantized equivalent of :class:`~torch.nn.PReLU`.
  
  Args:
      scale: quantization scale of the output tensor
      zero_point: quantization zero point of the output tensor
      num_parameters: number of parameters: 1, or the number of channels at input. Default: 1
class_docstr_tok: ["This","is","the","quantized","equivalent","of",":","class",":","`","~torch.nn.PReLU","`",".","Args",":","scale",":","quantization","scale","of","the","output","tensor","zero_point",":","quantization","zero","point","of","the","output","tensor","num_parameters",":","number","of","parameters",":","1",",","or","the","number","of","channels","at","input",".","Default",":","1"]
code: def from_reference(cls, mod, scale, zero_point):
      qprelu = cls(float(scale), int(zero_point), mod.num_parameters)
      float_wt = mod.weight.float()
      observer = mod.qconfig.weight()
      observer(float_wt)
      if observer.dtype != torch.quint8:
          warn(
              f"PReLU's weight observer should have dtype quint8 but got {observer.dtype}"
          )
      wt_scale, wt_zp = observer.calculate_qparams()
      qweight = torch.quantize_per_tensor(
          float_wt, float(wt_scale), int(wt_zp), torch.quint8
      )
      qprelu.set_weight(qweight)
      return qprelu
  
imports: import torch
  from warnings import warn
label_desc: Use image node_id 6 for calling the PReLU obj's underlying member method code with example usage: obj.from_reference(cls, mod, scale, zero_point) and returns: qprelu