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 forward(self, input: torch.Tensor) -> torch.Tensor:
      return torch.ops.quantized.prelu(
          input, self.weight, self.scale, self.zero_point
      )
  
imports: import torch
  from warnings import warn
label_desc: Use image node_id 3 for calling the PReLU obj's underlying member method code with example usage: obj.forward(input) and returns: torch