%strides = "tf.Const"() {value = dense<[3, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
    // expected-error @below {{'tf.XlaConvV2' op Expected output feature dimension (value 9) to be divisible by feature_group_count (value 2).}}

  }];

  let arguments = (ins
    Arg<TF_Tensor, [{4-D tensor with shape
`[batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
  depth]`. Note that the batch size of the input tensor must be divisible by
`block_size * block_size`.}]>:$input,
    Arg<TF_I32OrI64Tensor, [{2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
how many elements to crop from the intermediate result across the spatial

func generate(ch chan<- int) {
	for i := 2; ; i++ {
		ch <- i  // Send 'i' to channel 'ch'.
	}
}

// Copy the values from channel 'src' to channel 'dst',
// removing those divisible by 'prime'.
func filter(src <-chan int, dst chan<- int, prime int) {
	for i := range src {  // Loop over values received from 'src'.
		if i%prime != 0 {
			dst <- i  // Send 'i' to channel 'dst'.
		}

func generate(ch chan<- int) {
	for i := 2; ; i++ {
		ch <- i  // Send 'i' to channel 'ch'.
	}
}

// Copy the values from channel 'src' to channel 'dst',
// removing those divisible by 'prime'.
func filter(src <-chan int, dst chan<- int, prime int) {
	for i := range src {  // Loop over values received from 'src'.
		if i%prime != 0 {
			dst <- i  // Send 'i' to channel 'dst'.
		}