code: def test_dnnlowp_quantize(self, size, is_empty, absorb, gc, dc):
      if is_empty:
          size = 0
      min_ = -10.0
      max_ = 20.0
      X = (np.random.rand(size) * (max_ - min_) + min_).astype(
          np.float32
      )
      X_min = 0 if X.size == 0 else X.min()
      X_max = 1 if X.size == 0 else X.max()
      X_scale = (max(X_max, 0) - min(X_min, 0)) / 255
      X_zero = np.round(-X_min / X_scale)
  
      op_type_list = ["Quantize", "Int8Quantize"]
      engine = "DNNLOWP"
  
      for op_type in op_type_list:
          net = core.Net("test_net")
  
          quantize = core.CreateOperator(
              op_type, ["X"], ["X_q"], engine=engine, device_option=gc
          )
          net.Proto().op.extend([quantize])
  
          dnnlowp_pybind11.CreateInt8QuantParamsBlob(
              "quant_param", float(X_scale), int(X_zero)
          )
          quantize_2 = core.CreateOperator(
              op_type,
              ["X", "quant_param"],
              ["X_q_2"],
              engine=engine,
              device_option=gc,
          )
          net.Proto().op.extend([quantize_2])
          if absorb:
              net_str = dnnlowp_pybind11.freeze_quantization_params(
                  net.Proto().SerializeToString()
              )
              net.Proto().ParseFromString(net_str)
          workspace.FeedBlob("X", X, device_option=gc)
          workspace.RunNetOnce(net)
          X_q = workspace.FetchInt8Blob("X_q")[0]
          X_q_2 = workspace.FetchInt8Blob("X_q_2")[0]
  
          # Dequantize results and measure quantization error against inputs
          X_dq = X_scale * (X_q - X_zero)
          X_dq_2 = X_scale * (X_q_2 - X_zero)
  
          # should be divided by 2 in an exact math, but divide by 1.9 here
          # considering finite precision in floating-point numbers
          atol = X_scale / 1.9
          np.testing.assert_allclose(X_dq, X, atol=atol, rtol=0)
          np.testing.assert_allclose(X_dq_2, X, atol=atol, rtol=0)
  
file_path: pytorch/caffe2/quantization/server/quantize_dnnlowp_op_test.py
imports: import caffe2.python.hypothesis_test_util
  import hypothesis.strategies
  import numpy
  from caffe2.python import core, dyndep, workspace
  from caffe2.quantization.server import dnnlowp_pybind11
  from hypothesis import given, settings
label_desc: Use image node_id 1 for calling the DNNLowPQuantizeOpTest obj's underlying member method code with example usage: obj.test_dnnlowp_quantize(size, is_empty, absorb, gc, dc) without return types