padding = "SAME",
    stride_h = 1 : i32,
    stride_w = 1 : i32
  } : (tensor<256x32x32x3xf32>, tensor<2x3x3x3xf32>, tensor<2xf32>) -> tensor<256x32x32x2xf32>
  %1 = "tfl.add"(%0, %cst) {fused_activation_function = "NONE"} : (tensor<256x32x32x2xf32>, tensor<1x1x1x2xf32>) -> tensor<256x32x32x2xf32>
  func.return %1 : tensor<256x32x32x2xf32>

  // CHECK-DAG: %cst = arith.constant dense<[2.000000e+00, 4.000000e+00]> : tensor<2xf32>

// -----

// CHECK-LABEL: func @testValidConv2D
func.func @testValidConv2D(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x16xf32>) -> tensor<256x32x32x16xf32> {
  %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x32x32x16xf32>
  func.return %0 : tensor<256x32x32x16xf32>
}

// -----

// CHECK-LABEL: func @testValidDynamicConv2D

  func.return %0 : tensor<256x32x32x16xf32>
}

// -----

func.func @testConv2D4DBias(tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<1x1x1x16xf32>) -> tensor<256x32x32x16xf32> {
^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<1x1x1x16xf32>):

// CHECK:           return %[[VAL_5]] : tensor<256x30x30x16xf32>
// CHECK:         }

  }

  // CHECK-LABEL: func @conv2d_unranked_filter
  func.func @conv2d_unranked_filter(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
    // CHECK: "tf.Conv2D"
    // CHECK-SAME: -> tensor<256x?x?x?xf32>
    %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<*xf32>) -> tensor<*xf32>
    func.return %0 : tensor<*xf32>
  }

// TFLite runtime restrictions.
// RUN: tf-opt %s -tfl-optimize | FileCheck %s

// CHECK-LABEL: fuseScalarAddIntoConv2dHalf
func.func @fuseScalarAddIntoConv2dHalf(%arg0: tensor<256x32x32x3xf16>, %arg1: tensor<16x3x3x3xf16>) -> tensor<256x8x7x16xf16> {
  %cst = arith.constant dense<1.5> : tensor<f16>

func.func @conv_explicit_paddings(%arg0: tensor<256x32x32x6xf32>, %arg1: tensor<3x3x3x16xf32>) -> tensor<256x9x7x16xf32> {

  // 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>