%3 = "tf.ReadVariableOp"(%arg2) : (tensor<!tf_type.resource<tensor<5xf32>>>) -> tensor<5xf32>
  %4 = "tf.ReadVariableOp"(%arg1) : (tensor<!tf_type.resource<tensor<3x3x1x5xf32>>>) -> tensor<3x3x1x5xf32>
  %5 = "tf.ReadVariableOp"(%arg3) : (tensor<!tf_type.resource<tensor<3920x10xf32>>>) -> tensor<3920x10xf32>

// RUN: tf-mlir-translate -mlir-to-graphdef %s -tf-graph-as-function -o - | FileCheck %s

func.func @main(%arg0: tensor<*x!tf_type.resource>, %arg1: tensor<*x!tf_type.resource<tensor<3x3x1x32xf32>>>, %arg2: tensor<*xf32>, %arg3: tensor<2x4x6x8xi32>) -> (tensor<f32>, tensor<f32>)
attributes {tf.entry_function = {inputs = "args_0,args_1,args_2,args_3", outputs = "rets_0_RetVal,rets_1_RetVal"}} {
  %graph:2 = tf_executor.graph {

// CHECK-LABEL: strips_attributes
// CHECK-NOT: tf
func.func @strips_attributes(%arg0: tensor<32x28x28x1xf32> {tf._user_specified_name = "x"},
                             %arg1: tensor<3x3x1x5xf32> {tf._user_specified_name = "w1"},
                             %arg2: tensor<5xf32> {tf._user_specified_name = "b1"},
                             %arg3: tensor<3920x10xf32> {tf._user_specified_name = "w2"},

func.func @DontFuseAddWithConvActivationFunc(%arg0: tensor<1x3x1x1xf32>) -> tensor<1x2x1x3xf32> {
  %cst = arith.constant dense<1.5> : tensor<1xf32>
  %cst_1 = arith.constant dense<0.0> : tensor<3xf32>
  %cst_2 = arith.constant dense<1.1> : tensor<3x2x1x1xf32>
  %0 = "tfl.add"(%arg0, %cst) {fused_activation_function = "RELU6"} : (tensor<1x3x1x1xf32>, tensor<1xf32>) -> tensor<1x3x1x1xf32>

// CHECK-LABEL: convolution_3d
func.func @convolution_3d(%arg0: tensor<1x4x28x28x1xf32>, %arg1: tensor<2x3x3x1x16xf32>) -> tensor<1x3x26x26x16xf32> {
  %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, 0, 1, 2, f]x[0, 1, 2, i, o]->[b, 0, 1, 2, f], window = {} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x4x28x28x1xf32>, tensor<2x3x3x1x16xf32>) -> tensor<1x3x26x26x16xf32>
  return %0 : tensor<1x3x26x26x16xf32>
}

  func.return %1 : tensor<1x1x1x16xf32>

// CHECK: %[[avgp:.*]] = "tfl.average_pool_2d"(%arg0)
// CHECK: %[[dq:.*]] = "tfl.dequantize"(%[[avgp]]) : (tensor<1x1x1x16x!quant.uniform<u8:f32, 7.812500e-03:128>>) -> tensor<1x1x1x16xf32>
// CHECK: return %[[dq]] : tensor<1x1x1x16xf32>
}

// CHECK-LABEL: QuantizeReshape2D

    %3 = "tf.Identity"(%2) {device = ""} : (tensor<1x3x1x1xf32>) -> tensor<1x3x1x1xf32>
    return %3 : tensor<1x3x1x1xf32>
  }

// CHECK-LABEL: func @multiple_quantizable_ops_in_graph

# and ops in the main graph are retained. In addition, check if subsequent
# functions are converted.

# CHECK:      func @main(%arg0: tensor<*x!tf_type.resource>, %arg1: tensor<*x!tf_type.resource<tensor<3x3x1x32xf32>>>, %arg2: tensor<*xf32>, %arg3: tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<*xf32>)
# CHECK-SAME: _xla_compile_device_type = "GPU"
# CHECK-SAME: allow_soft_placement
# CHECK-SAME: control_outputs = ""

  // CHECK: mhlo.convolution(%arg0, %arg1)
  // CHECK-SAME{LITERAL}: pad = [[6, 0], [3, 3]]

  %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "EXPLICIT", explicit_paddings = [0, 0, 6, 0, 3, 3, 0, 0], strides = [1, 4, 5, 1]} : (tensor<256x32x32x6xf32>, tensor<3x3x3x16xf32>) -> tensor<256x9x7x16xf32>

// CHECK:           %[[VAL_3:.*]] = arith.constant dense<[3, 8, 8, 16]> : tensor<4xi64>
// CHECK:           %[[VAL_4:.*]] = "tf.BroadcastTo"(%[[VAL_2]], %[[VAL_3]]) : (tensor<3x1x1x16xf32>, tensor<4xi64>) -> tensor<3x8x8x16xf32>
// CHECK:           return %[[VAL_4]] : tensor<3x8x8x16xf32>
// CHECK:         }