func.return %2 : tensor<1x32x32x16xf32>
}

// -----

// CHECK-LABEL: @test_conv2d_qi8

// RUN: tf-opt %s -canonicalize | FileCheck %s

// CHECK-LABEL: func @testShape
func.func @testShape(tensor<f32>, tensor<1x32x32x16xf32>, tensor<*xf32>) -> (tensor<0xi32>, tensor<?xi32>, tensor<?xi32>) {
^bb0(%arg0: tensor<f32>, %arg1: tensor<1x32x32x16xf32>, %arg2: tensor<*xf32>):

  // CHECK-DAG: tf.Const{{.*}} dense<> : tensor<0xi32>
  %0 = "tf.Shape"(%arg0) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<f32>) -> tensor<0xi32>

      // CHECK-OPT: %{{.*}} = stablehlo.custom_call @tf.ResizeBilinear(%arg0, %cst) {align_corners = false, device = "", half_pixel_centers = true} : (tensor<1x32x32x128xf32>, tensor<2xi32>) -> tensor<1x64x64x128xf32>
      %1 = "tf.ResizeBilinear"(%arg0, %0) {

    "tfl.yield"(%4, %5, %6, %7) : (tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>) -> ()
  }) : (tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>) -> (tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>)

// CHECK-DAG:           %[[VAL_5:.*]] = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32>
// CHECK-DAG:           %[[VAL_6:.*]] = "tfl.transpose"(%[[VAL_4]], %[[VAL_5]]) : (tensor<1x32x32x64xf32>, tensor<4xi32>) -> tensor<1x64x32x32xf32>
// CHECK:           return %[[VAL_6]] : tensor<1x64x32x32xf32>
// CHECK:         }

  %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<?x32x32x6xf32>, tensor<3x3x3x16xf32>) -> tensor<?x8x7x16xf32>
  func.return %0 : tensor<?x8x7x16xf32>
}

  %cst_0 = arith.constant dense<3.0> : tensor<f32>
  %cst_1 = arith.constant dense<0.166666666> : tensor<f32>
  %w = arith.constant dense<1.0> : tensor<1x3x3x16xf32>
  %b = arith.constant dense<10.0> : tensor<16xf32>
  %2 = "tfl.add"(%arg0, %cst_0) {fused_activation_function = "RELU6"} : (tensor<1x112x112x16xf32>, tensor<f32>) -> tensor<1x112x112x16xf32>

  // CHECK-LABEL: func @conv2d_dynamic_batch
  func.func @conv2d_dynamic_batch(%arg0: tensor<?x32x32x3xf32>, %arg1: tensor<3x3x3x16xf32>) -> tensor<*xf32> {
    // CHECK: "tf.Conv2D"
    // CHECK-SAME: -> tensor<?x32x32x16xf32>
    %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<?x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<*xf32>
    func.return %0 : tensor<*xf32>
  }