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 __init__(
      self,
      output_scale: float,
      output_zero_point: int,
      num_parameters: int = 1,
  ) -> None:
      super().__init__()
      self.num_parameters = num_parameters
      self.scale = output_scale
      self.zero_point = output_zero_point
      w = torch.randn(num_parameters, dtype=torch.float)
      qw = torch.quantize_per_tensor(
          w, scale=1.0, zero_point=0, dtype=torch.quint8
      )
      self.set_weight(qw)
  
imports: import torch
  from warnings import warn
label_desc: Use image node_id 1 to create a new PReLU object from inherited base classes: torch.nn with example: obj = PReLU(output_scale, output_zero_point, num_parameters)