code: def init(self, dims, dtype):
      X = (torch.rand(*dims) - 0.5) * 256
      scale = 1.0
      zero_point = 0
      self.qX = torch.quantize_per_tensor(
          X, scale=scale, zero_point=zero_point, dtype=dtype
      )
  
      self.inputs = {
          "qX": self.qX,
          "weight": torch.rand(*self.qX.size()[1:], dtype=torch.float),
          "bias": torch.rand(*self.qX.size()[1:], dtype=torch.float),
          "eps": 1e-5,
          "Y_scale": 0.1,
          "Y_zero_point": 0,
      }
  
imports: import torch
  import operator_benchmark
label_desc: Use image node_id 1 for calling the QLayerNormBenchmark obj's underlying member method code with example usage: obj.init(dims, dtype) without return types