code: def test_int8_elementwise_sum(
      self, size, input_channels, batch_size, inputs, inplace, gc, dc
  ):
      sum_fp32 = core.CreateOperator(
          "Sum",
          ["X_{}".format(i) for i in range(inputs)],
          ["X_0" if inplace else "Y"],
      )
      Xs = [
          np.random.rand(batch_size, input_channels, size, size).astype(
              np.float32
          )
          for _ in range(inputs)
      ]
  
      old_ws_name = workspace.CurrentWorkspace()
      workspace.SwitchWorkspace("_device_check_", True)
  
      Xi_scales = []
      Xi_zero_points = []
      for i, X in enumerate(Xs):
          workspace.FeedBlob("X_{}".format(i), X, dc[0])
          if X.min() >= 0:
              Xi_scales.append(np.absolute(X).max() / 0xFF)
              Xi_zero_points.append(0)
          else:
              Xi_scales.append(np.absolute(X).max() / 0x7F)
              Xi_zero_points.append(128)
  
      workspace.RunOperatorOnce(sum_fp32)
      Y = workspace.FetchBlob("X_0" if inplace else "Y")
  
      if Y.min() >= 0:
          Y_scale = np.absolute(Y).max() / 0xFF
          Y_zero_point = 0
      else:
          Y_scale = np.absolute(Y).max() / 0x7F
          Y_zero_point = 128
  
      workspace.ResetWorkspace()
  
      net = caffe2_pb2.NetDef()
      for i, Xi in enumerate(Xs):
          workspace.FeedBlob("Xi_{}".format(i), Xi, dc[1])
          sw2nhwc = core.CreateOperator(
              "NCHW2NHWC",
              ["Xi_{}".format(i)],
              ["Xi_{}_nhwc".format(i)],
              device_option=dc[1],
          )
          quantize = core.CreateOperator(
              "Int8Quantize",
              ["Xi_{}_nhwc".format(i)],
              ["Xi_{}_quantized".format(i)],
              engine="DNNLOWP",
              device_option=dc[1],
              Y_zero_point=Xi_zero_points[i],
              Y_scale=Xi_scales[i],
          )
          net.op.extend([sw2nhwc, quantize])
  
      sum = core.CreateOperator(
          "Int8Sum",
          ["Xi_{}_quantized".format(i) for i in range(inputs)],
          ["Xi_0_quantized" if inplace else "Y_quantized"],
          engine="DNNLOWP",
          device_option=dc[1],
          Y_zero_point=Y_zero_point,
          Y_scale=Y_scale,
      )
  
      dequantize = core.CreateOperator(
          "Int8Dequantize",
          ["Xi_0_quantized" if inplace else "Y_quantized"],
          ["Y_nhwc"],
          engine="DNNLOWP",
          device_option=dc[1],
      )
  
      sw2nchw = core.CreateOperator(
          "NHWC2NCHW", ["Y_nhwc"], ["Y_out"], device_option=dc[1]
      )
  
      net.op.extend([sum, dequantize, sw2nchw])
      workspace.RunNetOnce(net)
      Y_out = workspace.FetchBlob("Y_out")
  
      MSE = np.square(np.subtract(Y, Y_out)).mean()
      if MSE > 0.005:
          print(Y.flatten())
          print(Y_out.flatten())
          print(np.max(np.abs(Y_out - Y)))
          print("MSE", MSE)
          self.assertTrue(False)
  
      workspace.SwitchWorkspace(old_ws_name)
  
file_path: pytorch/caffe2/python/ideep/elementwise_sum_op_test.py
imports: import unittest
  import hypothesis.strategies
  from hypothesis import given
  import numpy
  from caffe2.proto import caffe2_pb2
  from caffe2.python import core, workspace
  import caffe2.python.hypothesis_test_util
  import caffe2.python.ideep_test_util
label_desc: Use image node_id 3 for calling the ElementwiseSumTest obj's underlying member method code with example usage: obj.test_int8_elementwise_sum(size, input_channels, batch_size, inputs, inplace, gc, dc) without return types