// CHECK-DAG: %[[MAX:.*]] = mhlo.bitcast_convert %arg1 : (tensor<3x2xi32>) -> tensor<3x2x!quant.uniform<i32:f32:1, {2.000000e+00:4,2.000000e+00:4}>>
  // CHECK-DAG: %[[INPUT_1:.*]] = chlo.broadcast_maximum
  // CHECK: chlo.broadcast_minimum %[[INPUT_1]], %[[MAX]]
  %res = "tf.UniformQuantizedClipByValue"(%input, %min, %max, %scales, %zps) {
      quantization_axis = 1 : i64,
      quantization_min_val = -2147483648 : i64,

    // CHECK: %[[DOT:.*]] = stablehlo.dot_general %arg0, %arg1
    // CHECK: %[[DQ:.*]] = stablehlo.uniform_dequantize %[[DOT]]
    // CHECK: %[[MAX:.*]] = chlo.broadcast_maximum %[[DQ]], %[[MIN]]

func.func @relu6(%arg0: tensor<1xi32>) -> tensor<1xi32> {
  %0 = mhlo.constant dense<0> : tensor<i32>
  %1 = mhlo.constant dense<6> : tensor<i32>
  %2 = "chlo.broadcast_minimum"(%arg0, %1) {broadcast_dimensions = array<i64>} : (tensor<1xi32>, tensor<i32>) -> tensor<1xi32>
  %3 = "chlo.broadcast_maximum"(%2, %0) {broadcast_dimensions = array<i64>} : (tensor<1xi32>, tensor<i32>) -> tensor<1xi32>
  func.return %3 : tensor<1xi32>
}

// -----

// CHECK-LABEL: func @relu
func.func @relu(%arg0: tensor<1xi32>) -> tensor<1xi32> {
  // CHECK: %[[ZERO:.*]] = mhlo.constant dense<0> : tensor<i32>
  // CHECK: chlo.broadcast_maximum %[[ZERO]], %arg0 {broadcast_dimensions = array<i64>} : (tensor<i32>, tensor<1xi32>) -> tensor<1xi32>
  %0 = "tf.Relu"(%arg0) : (tensor<1xi32>) -> tensor<1xi32>
  func.return %0: tensor<1xi32>
}

// -----