%1 = "tf.Maximum"(%0, %cst_0) : (tensor<1x3x4x2xf32>, tensor<f32>) -> tensor<1x3x4x2xf32>
  %2 = "tf.Minimum"(%1, %cst_1) : (tensor<1x3x4x2xf32>, tensor<f32>) -> tensor<1x3x4x2xf32>
  func.return %2 : tensor<1x3x4x2xf32>

        _stablehlo_module_attrs = {}, _tfl_quant_trait = "fully_quantizable",
        device = ""
      } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32>
    return %0 : tensor<1x3x4x2xf32>
  }

  // CHECK: func.func private @qdq_for_conv_weight_per_channel_default(%[[ARG0:.+]]: tensor<1x3x4x3xf32>)
  // CHECK: %[[CST:.+]] = "tf.Const"() <{value = dense<3.000000e-01> : tensor<2x3x3x2xf32>}> : () -> tensor<2x3x3x2xf32>

// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[TRANSPOSE_0]], %[[CONST]]) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = {{\[\[}}1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x4x4x8xf32>, tensor<3x3x8x8xf32>) -> tensor<1x4x4x8xf32>

// CHECK:           %[[VAL_6:.*]] = "tfl.slice"(%[[VAL_5]], %[[VAL_1]], %[[VAL_2]]) : (tensor<1x4x384x32xf32>, tensor<4xi32>, tensor<4xi32>) -> tensor<1x1x384x32xf32>
// CHECK:           %[[VAL_7:.*]] = "tfl.reshape"(%[[VAL_6]], %[[VAL_4]]) : (tensor<1x1x384x32xf32>, tensor<3xi32>) -> tensor<1x384x32xf32>
// CHECK:           return %[[VAL_7]] : tensor<1x384x32xf32>
// CHECK:         }

// -----

  %0 = call @XlaCallModule__resize_0(%arg0) : (tensor<1x2x2x10xf32>) -> tensor<1x4x4x10xf32>
  return %0 : tensor<1x4x4x10xf32>
}
func.func private @XlaCallModule__resize_0(%arg0: tensor<1x2x2x10xf32>) -> (tensor<1x4x4x10xf32>) {
  %0 = mhlo.constant dense<2> : tensor<i32>
  %1 = mhlo.constant dense<0> : tensor<i32>
  %2 = mhlo.constant dense<4.000000e+00> : tensor<f32>

  %0 = "tfl.slice"(%arg0, %arg1, %arg2) : (tensor<4x384x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x384x32xf32>
  func.return %0 : tensor<1x384x32xf32>
}

// PARTIAL:       func @simple_test(%[[VAL_0:.*]]: tensor<4x384x32xf32>, %[[VAL_1:.*]]: tensor<3xi32>, %[[VAL_2:.*]]: tensor<3xi32>) -> tensor<1x384x32xf32> attributes {tac.interface_name = "func1"} {

  %0 = stablehlo.constant dense<2.000000e+00> : tensor<1x4x3x3xf32>
  %1 = stablehlo.transpose %arg0, dims = [0, 3, 1, 2] : (tensor<1x3x3x4xf32>) -> tensor<1x4x3x3xf32>
  %2 = stablehlo.add %1, %0 : tensor<1x4x3x3xf32>
  return %2 : tensor<1x4x3x3xf32>
}
// CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x3x3x4xf32>) -> tensor<1x4x3x3xf32>
// CHECK-DAG: %[[CONST_0:.+]] = stablehlo.constant

func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} {
  %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>
  %bias = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>

func.func @float_conv_strides_equals_to_dilations(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
  %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", device = "", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>

// CHECK-NEXT: %[[BIASADD:.*]] = "tf.BiasAdd"(%[[CONV2D]], %[[CONST_0]]) <{data_format = "NHWC"}> : (tensor<1x3x2x2xf32>, tensor<2xf32>) -> tensor<1x3x2x2xf32>
// CHECK-NEXT: return %[[BIASADD]] : tensor<1x3x2x2xf32>

// -----

func.func @not_convert_add_to_biasadd(%arg0: tensor<1x3x4x3xf32>) -> (tensor<1x3x2x3xf32>) {