// CHECK-SAME:                         %[[VAL_1:.*]]: tensor<3x3x207x16xf32>) -> tensor<1x8x8x16xf32> {
// CHECK:           %[[VAL_2:.*]] = "tf.Conv2D"(%[[VAL_0]], %[[VAL_1]]) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> : (tensor<1x8x8x207xf32>, tensor<3x3x207x16xf32>) -> tensor<1x8x8x16xf32>
// CHECK:           return %[[VAL_2]] : tensor<1x8x8x16xf32>

// -----

func.func @testConv2D(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x2x16xf32>) -> tensor<256x32x32x16xf32> {
  // expected-error @+1 {{requires the number of input channels to be divisible by the number of filter input channels; found 3 and 2, respectively}}
  %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x2x16xf32>) -> tensor<256x32x32x16xf32>
  func.return %0 : tensor<256x32x32x16xf32>