code: def init(self, dims, num_groups, dtype):
      X = (torch.rand(*dims) - 0.5) * 256
      num_channels = dims[1]
      scale = 1.0
      zero_point = 0
  
      self.inputs = {
          "qX": torch.quantize_per_tensor(
              X, scale=scale, zero_point=zero_point, dtype=dtype
          ),
          "num_groups": num_groups,
          "weight": torch.rand(num_channels, dtype=torch.float),
          "bias": torch.rand(num_channels, 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 QGroupNormBenchmark obj's underlying member method code with example usage: obj.init(dims, num_groups, dtype) without return types