func.func private @f(%arg0: f32, %arg1: f32) -> (f32, f32) {
  %0 = "some_op"(%arg0) : (f32) -> f32
  return %0, %arg1 : f32, f32
}

// CHECK-LABEL: @removes_second_passthrough_arg
func.func @removes_second_passthrough_arg(%arg0: f32, %arg1: f32) -> (f32, f32) {
  // CHECK: %0 = call @f(%arg0)
  %0, %1 = call @f(%arg0, %arg1) : (f32, f32) -> (f32, f32)
  // CHECK: return %0, %arg1
  return %0, %1 : f32, f32
}

// -----

    // CHECK-DAG: %[[CST_1:.*]] = "tf.Const"() <{value = dense<3> : tensor<i32>}> : () -> tensor<i32>
    // CHECK-NEXT: %[[UQ:.*]] = "tf.UniformQuantize"(%arg0, %cst, %cst_0) <{quantization_axis = -1 : i64, quantization_max_val = 127 : i64, quantization_min_val = -128 : i64}> : (tensor<1xf32>, tensor<f32>, tensor<i32>) -> tensor<1x!tf_type.qint8>

// CHECK:         %[[ADD:.*]] = f32[] add(f32[] %[[DIV]], f32[]
// CHECK:         %[[CLAMP:.*]] = f32[] clamp(f32[] %[[MIN:.*]], f32[] %[[ADD]],
// CHECK:         %[[ROUND:.*]] = f32[] round-nearest-even(f32[] %[[CLAMP]])
// CHECK:         %[[CONVERT_0:.*]] = u8[] convert(f32[] %[[ROUND]])
// CHECK:         %[[CONVERT_1:.*]] = s32[] convert(u8[] %[[CONVERT_0]])
// CHECK:         %[[SUB:.*]] = s32[] subtract(s32[] %[[CONVERT_1]],

  %requant = stablehlo.uniform_quantize %conv : (tensor<1x3x4x2x!quant.uniform<i32:f32, 5.8949912267181218E-5>>) -> tensor<1x3x4x2x!quant.uniform<i8:f32, 0.045673100153605144:-62>>

  %4 = "tfl.reshape"(%3, %cst) : (tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>, tensor<2xi32>) -> tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>>
  %5 = "tfl.softmax"(%4) {beta = 1.000000e+00 : f32} : (tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>>) -> tensor<1x401408x!quant.uniform<u8:f32, 3.906250e-03>>

// CHECK: %[[CUSTOM_AGGREGATOR_1:.*]], %[[MIN_1:.*]], %[[MAX_1:.*]], %[[HISTOGRAM_1:.*]] = "tf.CustomAggregator"

  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<15.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %rst = "tf.FakeQuantWithMinMaxVars"(%in, %mini, %maxi) {num_bits = 3, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %rst : tensor<8xf32>

  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v})
  attributes {tf_saved_model.exported_names = ["f"]} {
    %c0 = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
    "tf.AssignVariableOp"(%arg0, %c0) : (tensor<!tf_type.resource<tensor<f32>>>, tensor<f32>) -> ()
    func.return
  }

}

  %0 = stablehlo.add %arg0, %arg0 : tensor<f32>
  return %0 :  tensor<f32>
}

// -----

// CHECK-LABEL: mixed_shlo_tfl_shlo
func.func @mixed_shlo_tfl_shlo(%arg0 : tensor<f32>) -> (tensor<f32>) {
  // CHECK: %0 = "vhlo.abs_v1"(%arg0) : (tensor<f32>) -> tensor<f32>
  // CHECK-NEXT: %1 = tfl.add %0, %arg0 {fused_activation_function = "NONE"} : tensor<f32>
  // CHECK-NEXT: %2 = "vhlo.abs_v1"(%1) : (tensor<f32>) -> tensor<f32>

      f = @identity} : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>
  %2 = "tf.OptionalGetValue"(%1) : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<f32>
  %3 = "tf.OptionalNone"() : () -> tensor<!tf_type.variant<tensor<f32>>>
  return %2, %3 : tensor<f32>, tensor<!tf_type.variant<tensor<f32>>>
}

func.func private @identity(%arg0: tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>> {