func.return %rst : tensor<256x8x7x16xf32>

// CHECK: %[[CONSTANT0:.*]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<3x3x3x16xf32>}>
// CHECK: %[[QUANTIZE:.*]] = "quantfork.qcast"(%[[CONSTANT0]]) : (tensor<3x3x3x16xf32>) -> tensor<3x3x3x16x!quant.uniform<i4:f32, 1.000000e+00:-8>>

  func.return %rst : tensor<256x8x7x16xf32>

// CHECK: %[[CONSTANT0:.*]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<3x3x3x16xf32>}>
// CHECK: %[[QUANTIZE:.*]] = "quantfork.qcast"(%[[CONSTANT0]]) : (tensor<3x3x3x16xf32>) -> tensor<3x3x3x16x!quant.uniform<i8:f32, 1.000000e+00:-128>>

  %filter = arith.constant dense<2.0> : tensor<3x3x3x16xf32>
  %bias = arith.constant dense<3.0> : tensor<16xf32>
  %value = arith.constant dense<4.0> : tensor<16xf32>
  %0 = "tf.Conv2D"(%arg, %filter) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>

    %dimension = "tf.Const"() { value = dense<3> : tensor<1xi64> } : () -> tensor<1xi64>
    %6 = "tf.Sum"(%3, %dimension) { keep_dims = true }: (tensor<1x3x2x2xf32>, tensor<1xi64>) -> tensor<1x3x2x1xf32>
    return %5, %6 : tensor<1x3x2x2xf32>, tensor<1x3x2x1xf32>
  }

// CHECK-LABEL: func @conv_with_multiple_uses
// CHECK: %[[div:.*]] = "tf.Div"(%arg0
// CHECK: %[[add:.*]] = "tf.AddV2"(%[[div]]
// CHECK: %[[maximum:.*]] = "tf.Maximum"(%[[add]]

  %cst_3 = "tf.Const"() {value = dense<[[[[1.400000e+01]], [[-2.800000e+01]], [[4.200000e+01]]], [[[-5.600000e+01]], [[7.100000e+01]], [[-8.500000e+01]]], [[[9.900000e+01]], [[-1.130000e+02]], [[1.270000e+02]]]]> : tensor<3x3x1x1xf32>} : () -> tensor<3x3x1x1xf32>
  %cst_4 = "tf.Const"() {value = dense<-1.280000e+02> : tensor<f32>} : () -> tensor<f32>
  %cst_5 = "tf.Const"() {value = dense<0.00118110236> : tensor<1xf32>} : () -> tensor<1xf32>