return nil
		}
		return sysOp{sysInst, X0 + Reg(reg), true}

	case arg_Bt:
		return B0 + Reg(x&(1<<5-1))

	case arg_Dt:
		return D0 + Reg(x&(1<<5-1))

	case arg_Dt2:
		return D0 + Reg((x>>10)&(1<<5-1))

	case arg_Ht:
		return H0 + Reg(x&(1<<5-1))

	case arg_immediate_0_63_immh_immb__UIntimmhimmb64_8:
		immh := (x >> 19) & (1<<4 - 1)

// CHECK:  %[[RSQRT:.*]] = "tf.Rsqrt"(%[[VARIANCE]])
// CHECK:  %[[MUL1:.*]] = "tf.Mul"(%[[ARG_T:.*]], %[[RSQRT]])
// CHECK:  %[[MUL2:.*]] = "tf.Mul"(%[[ARG_M:.*]], %[[RSQRT]])
// CHECK:  %[[SUB:.*]] = "tf.Sub"(%[[ARG_BETA:.*]], %[[MUL2]])
// CHECK:  %[[RESULT:.*]] = "tf.Add"(%[[MUL1]], %[[SUB]])
// CHECK:  return %[[RESULT]]
}

func.func @batchNormWithGlobalNormalizationWithScaleAfterNormalization(

    CompareTensors(out0, expected_out0);
    CompareTensors(out1, expected_out1);
  }

  void CompareTensors(TF_Tensor* a, TF_Tensor* b) {
    float* a_data = static_cast<float*>(TF_TensorData(a));
    float* b_data = static_cast<float*>(TF_TensorData(b));
    EXPECT_EQ(*a_data, *b_data);
  }

  void AddGradients(bool grad_inputs_provided, const char* prefix,
                    TF_Output* inputs, int ninputs, TF_Output* outputs,